{"id":6088,"date":"2025-05-21T12:43:36","date_gmt":"2025-05-21T12:43:36","guid":{"rendered":"https:\/\/hetida.io\/blog\/energy-forecasts-with-hetida-designer\/"},"modified":"2025-05-21T13:54:36","modified_gmt":"2025-05-21T13:54:36","slug":"energy-forecasts-with-hetida-designer","status":"publish","type":"post","link":"https:\/\/hetida.io\/en\/blog\/energy-forecasts-with-hetida-designer\/","title":{"rendered":"Energy forecasts with hetida designer"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"6088\" class=\"elementor elementor-6088 elementor-6052\" data-elementor-post-type=\"post\">\n\t\t\t\t<div class=\"elementor-element elementor-element-3baf4066 e-flex e-con-boxed e-con e-parent\" data-id=\"3baf4066\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-26cc7325 elementor-widget elementor-widget-heading\" data-id=\"26cc7325\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h1 class=\"elementor-heading-title elementor-size-default\">Energy forecasts with hetida designer<\/h1>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-7d7a9409 elementor-widget elementor-widget-text-editor\" data-id=\"7d7a9409\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>Using hetida designer, the interactive Python workflow editor of the hetida platform, we are developing a model for forecasting the electricity consumption of an industrial company. In addition to the practical question, the main focus is on the possible applications and operation of hetida designer. <\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-46aeb5ea elementor-widget-divider--view-line elementor-widget elementor-widget-divider\" data-id=\"46aeb5ea\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"divider.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-divider\">\n\t\t\t<span class=\"elementor-divider-separator\">\n\t\t\t\t\t\t<\/span>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-6dae20f2 elementor-widget elementor-widget-heading\" data-id=\"6dae20f2\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">How can electricity consumption be forecast?<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-3186768c elementor-widget elementor-widget-text-editor\" data-id=\"3186768c\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\tNot only industrial companies, but also municipal utilities buy their electricity on the financial markets, sometimes years in advance. The more accurately consumption can be forecast for a specific point in time, the better. Less short-term and expensive additional purchases are necessary. Conversely, too much electricity that has already been purchased does not have to be resold at poorer conditions.   \n\nIntuitive factors or questions besides the time that (can) influence power consumption are:\n<ul>\n \t<li>Which day of the week is being considered? Does the day fall on a weekend? <\/li>\n \t<li>Are there school vacations on that day?<\/li>\n \t<li>Is it a public holiday?<\/li>\n<\/ul>\nWe look at the load profile of a manufacturing company in the Swiss canton of Aargau in 2016, which includes quarter-hourly electricity consumption values. Based on this data, we answer the following question:  \n<p data-renderer-start-pos=\"1245\"><strong data-renderer-mark=\"true\">What is the company&#8217;s electricity consumption on May 25, 2017 between 10 a.m. and 11 a.m.?<\/strong><\/p>\n<p data-renderer-start-pos=\"1245\">We work with the <a class=\"_mizu1p6i _1ah31bk5 _ra3xnqa1 _128m1bk5 _1cvmnqa1 _4davt94y _4bfu18uv _1hms8stv _ajmmnqa1 _vchhusvi _syaz14q2 _ect41gqc _1a3b18uv _4fpr8stv _5goinqa1 _f8pj14q2 _9oik18uv _1bnxglyw _jf4cnqa1 _30l314q2 _1nrm18uv _c2waglyw _1iohnqa1 _9h8h16c2 _1053w7te _1ienw7te _n0fxw7te _1vhvg3x0\" title=\"https:\/\/fuseki.com\/data-science\/hetida-designer\/\" href=\"https:\/\/fuseki.com\/data-science\/hetida-designer\/\" data-renderer-mark=\"true\">hetida designer<\/a> to predict storm consumption. We start our analysis by <em data-renderer-mark=\"true\">visualizing <\/em>the available data. In the next step, <em data-renderer-mark=\"true\">data preparation<\/em>, we expand the data set to include the above-mentioned temporal influencing factors. We then train a model that reconstructs the load profile from 2016 as accurately as possible. To do this, we consider <em data-renderer-mark=\"true\">linear regression<\/em> and a <em data-renderer-mark=\"true\">random forest <\/em>algorithm, a basic machine learning tool. Finally, we use the random forest algorithm, trained on the 2016 data, to predict May 25, 2017.     <\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-2fbd68f0 elementor-widget elementor-widget-heading\" data-id=\"2fbd68f0\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Data visualization<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-4f375f43 elementor-widget elementor-widget-image\" data-id=\"4f375f43\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t<figure class=\"wp-caption\">\n\t\t\t\t\t\t\t\t\t\t\t<a href=\"https:\/\/hetida.io\/wp-content\/uploads\/2025\/05\/hetida_designer_lastgang_roh.webp\" data-elementor-open-lightbox=\"yes\" data-elementor-lightbox-title=\"hetida_designer_lastgang_roh\" data-e-action-hash=\"#elementor-action%3Aaction%3Dlightbox%26settings%3DeyJpZCI6NjA2MCwidXJsIjoiaHR0cHM6XC9cL2hldGlkYS5pb1wvd3AtY29udGVudFwvdXBsb2Fkc1wvMjAyNVwvMDVcL2hldGlkYV9kZXNpZ25lcl9sYXN0Z2FuZ19yb2gud2VicCJ9\">\n\t\t\t\t\t\t\t<img fetchpriority=\"high\" decoding=\"async\" width=\"2041\" height=\"395\" src=\"https:\/\/hetida.io\/wp-content\/uploads\/2025\/05\/hetida_designer_lastgang_roh.webp\" class=\"attachment-full size-full wp-image-6060\" alt=\"Abbidlung der Rohdaten der Lastganganalyse\" srcset=\"https:\/\/hetida.io\/wp-content\/uploads\/2025\/05\/hetida_designer_lastgang_roh.webp 2041w, https:\/\/hetida.io\/wp-content\/uploads\/2025\/05\/hetida_designer_lastgang_roh-300x58.webp 300w, https:\/\/hetida.io\/wp-content\/uploads\/2025\/05\/hetida_designer_lastgang_roh-1024x198.webp 1024w, https:\/\/hetida.io\/wp-content\/uploads\/2025\/05\/hetida_designer_lastgang_roh-768x149.webp 768w, https:\/\/hetida.io\/wp-content\/uploads\/2025\/05\/hetida_designer_lastgang_roh-1536x297.webp 1536w\" sizes=\"(max-width: 2041px) 100vw, 2041px\" \/>\t\t\t\t\t\t\t\t<\/a>\n\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\">Plot of the load profile from 2016<\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-3870db77 elementor-widget elementor-widget-text-editor\" data-id=\"3870db77\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\tThe typical five-day week for an industrial company can be identified. Occasionally, for example in May, July or at the end of December, different structures can be identified. We first want to explore and explain these and then take them into account in the forecast for 2017.  \t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-6aa9f3d elementor-widget elementor-widget-image\" data-id=\"6aa9f3d\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t<figure class=\"wp-caption\">\n\t\t\t\t\t\t\t\t\t\t\t<a href=\"https:\/\/hetida.io\/wp-content\/uploads\/2025\/05\/hetida_designer_visualisierung_lastgang.webp\" data-elementor-open-lightbox=\"yes\" data-elementor-lightbox-title=\"hetida_designer_visualisierung_lastgang\" data-e-action-hash=\"#elementor-action%3Aaction%3Dlightbox%26settings%3DeyJpZCI6NjA2MiwidXJsIjoiaHR0cHM6XC9cL2hldGlkYS5pb1wvd3AtY29udGVudFwvdXBsb2Fkc1wvMjAyNVwvMDVcL2hldGlkYV9kZXNpZ25lcl92aXN1YWxpc2llcnVuZ19sYXN0Z2FuZy53ZWJwIn0%3D\">\n\t\t\t\t\t\t\t<img decoding=\"async\" width=\"2043\" height=\"217\" src=\"https:\/\/hetida.io\/wp-content\/uploads\/2025\/05\/hetida_designer_visualisierung_lastgang.webp\" class=\"attachment-full size-full wp-image-6062\" alt=\"Visualisierung des hetida designer Workflows f\u00fcr die Lastganganalyse.\" srcset=\"https:\/\/hetida.io\/wp-content\/uploads\/2025\/05\/hetida_designer_visualisierung_lastgang.webp 2043w, https:\/\/hetida.io\/wp-content\/uploads\/2025\/05\/hetida_designer_visualisierung_lastgang-300x32.webp 300w, https:\/\/hetida.io\/wp-content\/uploads\/2025\/05\/hetida_designer_visualisierung_lastgang-1024x109.webp 1024w, https:\/\/hetida.io\/wp-content\/uploads\/2025\/05\/hetida_designer_visualisierung_lastgang-768x82.webp 768w, https:\/\/hetida.io\/wp-content\/uploads\/2025\/05\/hetida_designer_visualisierung_lastgang-1536x163.webp 1536w\" sizes=\"(max-width: 2043px) 100vw, 2043px\" \/>\t\t\t\t\t\t\t\t<\/a>\n\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\">Workflow for visualizing the load profile<\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-f8ab30d elementor-widget elementor-widget-text-editor\" data-id=\"f8ab30d\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\tFirst, the data is read from the database as a time series (see following figure) and converted into a data frame. At the same time, the power consumption values are given the name \u2018Values\u2019. The second component plots the data.  \t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-658c3e8 elementor-widget elementor-widget-image\" data-id=\"658c3e8\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t<figure class=\"wp-caption\">\n\t\t\t\t\t\t\t\t\t\t\t<a href=\"https:\/\/hetida.io\/wp-content\/uploads\/2025\/05\/hetida_designer_execute_visualisierung.webp\" data-elementor-open-lightbox=\"yes\" data-elementor-lightbox-title=\"hetida_designer_execute_visualisierung\" data-e-action-hash=\"#elementor-action%3Aaction%3Dlightbox%26settings%3DeyJpZCI6NjA2NCwidXJsIjoiaHR0cHM6XC9cL2hldGlkYS5pb1wvd3AtY29udGVudFwvdXBsb2Fkc1wvMjAyNVwvMDVcL2hldGlkYV9kZXNpZ25lcl9leGVjdXRlX3Zpc3VhbGlzaWVydW5nLndlYnAifQ%3D%3D\">\n\t\t\t\t\t\t\t<img decoding=\"async\" width=\"1734\" height=\"358\" src=\"https:\/\/hetida.io\/wp-content\/uploads\/2025\/05\/hetida_designer_execute_visualisierung.webp\" class=\"attachment-full size-full wp-image-6064\" alt=\"Visualisierung der Input- und Output-Parameter im hetida designer\" srcset=\"https:\/\/hetida.io\/wp-content\/uploads\/2025\/05\/hetida_designer_execute_visualisierung.webp 1734w, https:\/\/hetida.io\/wp-content\/uploads\/2025\/05\/hetida_designer_execute_visualisierung-300x62.webp 300w, https:\/\/hetida.io\/wp-content\/uploads\/2025\/05\/hetida_designer_execute_visualisierung-1024x211.webp 1024w, https:\/\/hetida.io\/wp-content\/uploads\/2025\/05\/hetida_designer_execute_visualisierung-768x159.webp 768w, https:\/\/hetida.io\/wp-content\/uploads\/2025\/05\/hetida_designer_execute_visualisierung-1536x317.webp 1536w\" sizes=\"(max-width: 1734px) 100vw, 1734px\" \/>\t\t\t\t\t\t\t\t<\/a>\n\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\">Importing data from the database.<\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-20b46c1f elementor-widget elementor-widget-heading\" data-id=\"20b46c1f\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Data preparation<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-355cbc5 elementor-widget elementor-widget-text-editor\" data-id=\"355cbc5\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\tWe assign the corresponding <em data-renderer-mark=\"true\">month<\/em>, <em data-renderer-mark=\"true\">day of the week<\/em> and <em data-renderer-mark=\"true\">hour <\/em>of the day to each consumption value. For a more precise analysis of the underlying time data, we choose a representation using sine and cosine. This makes it possible to specifically consider cyclical dependencies between the respective time specifications. We also decide for each value whether it falls on a <em data-renderer-mark=\"true\">weekend<\/em>, a <em data-renderer-mark=\"true\">public holiday<\/em> or a <em data-renderer-mark=\"true\">school vacation day<\/em>. The data set generated in this way forms the basis for developing a model to predict electricity consumption for May 25, 2017.    \t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-1392ebe elementor-widget elementor-widget-image\" data-id=\"1392ebe\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t<figure class=\"wp-caption\">\n\t\t\t\t\t\t\t\t\t\t\t<a href=\"https:\/\/hetida.io\/wp-content\/uploads\/2025\/05\/hetida_designer_workflow_datenaufbereitung.webp\" data-elementor-open-lightbox=\"yes\" data-elementor-lightbox-title=\"hetida_designer_workflow_datenaufbereitung\" data-e-action-hash=\"#elementor-action%3Aaction%3Dlightbox%26settings%3DeyJpZCI6NjA2NiwidXJsIjoiaHR0cHM6XC9cL2hldGlkYS5pb1wvd3AtY29udGVudFwvdXBsb2Fkc1wvMjAyNVwvMDVcL2hldGlkYV9kZXNpZ25lcl93b3JrZmxvd19kYXRlbmF1ZmJlcmVpdHVuZy53ZWJwIn0%3D\">\n\t\t\t\t\t\t\t<img loading=\"lazy\" decoding=\"async\" width=\"1950\" height=\"1409\" src=\"https:\/\/hetida.io\/wp-content\/uploads\/2025\/05\/hetida_designer_workflow_datenaufbereitung.webp\" class=\"attachment-full size-full wp-image-6066\" alt=\"Darstellung des hetida designer Workflows zur Datenaufbereitung\" srcset=\"https:\/\/hetida.io\/wp-content\/uploads\/2025\/05\/hetida_designer_workflow_datenaufbereitung.webp 1950w, https:\/\/hetida.io\/wp-content\/uploads\/2025\/05\/hetida_designer_workflow_datenaufbereitung-300x217.webp 300w, https:\/\/hetida.io\/wp-content\/uploads\/2025\/05\/hetida_designer_workflow_datenaufbereitung-1024x740.webp 1024w, https:\/\/hetida.io\/wp-content\/uploads\/2025\/05\/hetida_designer_workflow_datenaufbereitung-768x555.webp 768w, https:\/\/hetida.io\/wp-content\/uploads\/2025\/05\/hetida_designer_workflow_datenaufbereitung-1536x1110.webp 1536w\" sizes=\"(max-width: 1950px) 100vw, 1950px\" \/>\t\t\t\t\t\t\t\t<\/a>\n\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\">Data preparation with the hetida designer<\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-605717fe elementor-widget elementor-widget-text-editor\" data-id=\"605717fe\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\tThe data frame generated above is first extended to include the cyclical representation of the time information (Circular Representation of Time Components). Then the information is added as to whether a consumption value falls on a weekend (Weekend). The three other components decide whether a value falls on a school vacation day, a public holiday, or both a weekend and a public holiday. These three components can be used for any geographical region, with specific inputs in each case. For our company from Switzerland, for example, we select \u201ccountry = CH, province = AG, state = None, year = 2016\u201d.    \t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-325439e elementor-widget elementor-widget-heading\" data-id=\"325439e\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Linear regression<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-370b9f28 elementor-widget elementor-widget-text-editor\" data-id=\"370b9f28\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\tIn addition to the regression of the consumption values, the underlying company is primarily interested in how well the model depicts the load profile. We analyze the <em data-renderer-mark=\"true\">quality of the model<\/em> using the R\u00b2 value. We also output the coefficients of the influencing variables to get an impression of which of these factors have a particularly strong influence on electricity consumption.  \t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-1b67a98 elementor-widget elementor-widget-image\" data-id=\"1b67a98\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t<figure class=\"wp-caption\">\n\t\t\t\t\t\t\t\t\t\t\t<a href=\"https:\/\/hetida.io\/wp-content\/uploads\/2025\/05\/hetida_designer_model_vorhersage.webp\" data-elementor-open-lightbox=\"yes\" data-elementor-lightbox-title=\"hetida_designer_model_vorhersage\" data-e-action-hash=\"#elementor-action%3Aaction%3Dlightbox%26settings%3DeyJpZCI6NjA2OCwidXJsIjoiaHR0cHM6XC9cL2hldGlkYS5pb1wvd3AtY29udGVudFwvdXBsb2Fkc1wvMjAyNVwvMDVcL2hldGlkYV9kZXNpZ25lcl9tb2RlbF92b3JoZXJzYWdlLndlYnAifQ%3D%3D\">\n\t\t\t\t\t\t\t<img loading=\"lazy\" decoding=\"async\" width=\"2135\" height=\"895\" src=\"https:\/\/hetida.io\/wp-content\/uploads\/2025\/05\/hetida_designer_model_vorhersage.webp\" class=\"attachment-full size-full wp-image-6068\" alt=\"hetida designer Workflows zur Vorhersage\" srcset=\"https:\/\/hetida.io\/wp-content\/uploads\/2025\/05\/hetida_designer_model_vorhersage.webp 2135w, https:\/\/hetida.io\/wp-content\/uploads\/2025\/05\/hetida_designer_model_vorhersage-300x126.webp 300w, https:\/\/hetida.io\/wp-content\/uploads\/2025\/05\/hetida_designer_model_vorhersage-1024x429.webp 1024w, https:\/\/hetida.io\/wp-content\/uploads\/2025\/05\/hetida_designer_model_vorhersage-768x322.webp 768w, https:\/\/hetida.io\/wp-content\/uploads\/2025\/05\/hetida_designer_model_vorhersage-1536x644.webp 1536w, https:\/\/hetida.io\/wp-content\/uploads\/2025\/05\/hetida_designer_model_vorhersage-2048x859.webp 2048w\" sizes=\"(max-width: 2135px) 100vw, 2135px\" \/>\t\t\t\t\t\t\t\t<\/a>\n\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\">Workflow of the entire linear regression.<\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-5227d73 elementor-widget elementor-widget-text-editor\" data-id=\"5227d73\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\tThe processed data is first prepared for the regression (\u2018train, test, split\u2019). For this purpose, the data is split into training and test data. We pass the \u2018Values\u2019 column as the target variable (label) of the regression. We select 20 percent of the data (test_size = 0.2) as test data and train the model on the remaining 80 percent. This training takes place in the next step (\u2018Linear Regression &#8211; Trained Model\u2019). In the upper strand of the workflow, predicted values are then generated on the trained model (\u2018Predit Sklearn Trained Model\u2019). The regression can occasionally predict negative values. As this makes no sense in terms of content, we set these values to zero (\u2018Negative to Zero\u2019). Finally, the predicted values are visualized together with the test data. The lower two components generate the coefficients (\u2018Linear Regression &#8211; Coefficients\u2019) and the R\u00b2 value (\u2018Linear Regression &#8211; Goodness of Fit\u2019) of the linear regression.         \t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-aff276c elementor-widget elementor-widget-image\" data-id=\"aff276c\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t<figure class=\"wp-caption\">\n\t\t\t\t\t\t\t\t\t\t\t<a href=\"https:\/\/hetida.io\/wp-content\/uploads\/2025\/05\/hetida_designer_regression_result-1024x277-1.png\" data-elementor-open-lightbox=\"yes\" data-elementor-lightbox-title=\"hetida_designer_regression_result-1024x277\" data-e-action-hash=\"#elementor-action%3Aaction%3Dlightbox%26settings%3DeyJpZCI6NjA3MCwidXJsIjoiaHR0cHM6XC9cL2hldGlkYS5pb1wvd3AtY29udGVudFwvdXBsb2Fkc1wvMjAyNVwvMDVcL2hldGlkYV9kZXNpZ25lcl9yZWdyZXNzaW9uX3Jlc3VsdC0xMDI0eDI3Ny0xLnBuZyJ9\">\n\t\t\t\t\t\t\t<img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"277\" src=\"https:\/\/hetida.io\/wp-content\/uploads\/2025\/05\/hetida_designer_regression_result-1024x277-1.png\" class=\"attachment-full size-full wp-image-6070\" alt=\"Ergebnis der Regression\" srcset=\"https:\/\/hetida.io\/wp-content\/uploads\/2025\/05\/hetida_designer_regression_result-1024x277-1.png 1024w, https:\/\/hetida.io\/wp-content\/uploads\/2025\/05\/hetida_designer_regression_result-1024x277-1-300x81.png 300w, https:\/\/hetida.io\/wp-content\/uploads\/2025\/05\/hetida_designer_regression_result-1024x277-1-768x208.png 768w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/>\t\t\t\t\t\t\t\t<\/a>\n\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\">The results log after executing the above workflow<\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-27d18da elementor-widget elementor-widget-text-editor\" data-id=\"27d18da\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\tAs expected, the coefficients for weekends, school vacations and public holidays are strongly negative. On such days, the company consumes significantly less electricity. This information is particularly important for the subsequent forecast. At 0.82, the R\u00b2 value is already in the very good range.   \n\nThe question is: Should we now use the linear regression to predict May 25, 2017? Or can we improve the model or the R\u00b2 value even further? <p data-renderer-start-pos=\"5932\">The question is: Should we now use the linear regression to predict May 25, 2017? Or can we improve the model or the <em data-renderer-mark=\"true\">R\u00b2 value<\/em> even further? <\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-af89089 elementor-widget elementor-widget-heading\" data-id=\"af89089\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Random Forest<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-51972f22 elementor-widget elementor-widget-text-editor\" data-id=\"51972f22\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\tIn a second step, we pass the processed data set from 2016 to a <em data-renderer-mark=\"true\">random forest<\/em> algorithm. Once again, we analyze the <em data-renderer-mark=\"true\">quality of the model<\/em> using the R\u00b2 value. We also output the percentage influence of the influencing variables in descending order.  \t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-a9efe5d elementor-widget elementor-widget-image\" data-id=\"a9efe5d\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t<figure class=\"wp-caption\">\n\t\t\t\t\t\t\t\t\t\t\t<a href=\"https:\/\/hetida.io\/wp-content\/uploads\/2025\/05\/hetida_designer_workflow_random_forest.webp\" data-elementor-open-lightbox=\"yes\" data-elementor-lightbox-title=\"hetida_designer_workflow_random_forest\" data-e-action-hash=\"#elementor-action%3Aaction%3Dlightbox%26settings%3DeyJpZCI6NjA3MiwidXJsIjoiaHR0cHM6XC9cL2hldGlkYS5pb1wvd3AtY29udGVudFwvdXBsb2Fkc1wvMjAyNVwvMDVcL2hldGlkYV9kZXNpZ25lcl93b3JrZmxvd19yYW5kb21fZm9yZXN0LndlYnAifQ%3D%3D\">\n\t\t\t\t\t\t\t<img loading=\"lazy\" decoding=\"async\" width=\"2126\" height=\"736\" src=\"https:\/\/hetida.io\/wp-content\/uploads\/2025\/05\/hetida_designer_workflow_random_forest.webp\" class=\"attachment-full size-full wp-image-6072\" alt=\"Abbildung des Random-Forest-Workflows im hetida designer\" srcset=\"https:\/\/hetida.io\/wp-content\/uploads\/2025\/05\/hetida_designer_workflow_random_forest.webp 2126w, https:\/\/hetida.io\/wp-content\/uploads\/2025\/05\/hetida_designer_workflow_random_forest-300x104.webp 300w, https:\/\/hetida.io\/wp-content\/uploads\/2025\/05\/hetida_designer_workflow_random_forest-1024x354.webp 1024w, https:\/\/hetida.io\/wp-content\/uploads\/2025\/05\/hetida_designer_workflow_random_forest-768x266.webp 768w, https:\/\/hetida.io\/wp-content\/uploads\/2025\/05\/hetida_designer_workflow_random_forest-1536x532.webp 1536w, https:\/\/hetida.io\/wp-content\/uploads\/2025\/05\/hetida_designer_workflow_random_forest-2048x709.webp 2048w\" sizes=\"(max-width: 2126px) 100vw, 2126px\" \/>\t\t\t\t\t\t\t\t<\/a>\n\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\">Workflow of random forest<\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-5783d68f elementor-widget elementor-widget-text-editor\" data-id=\"5783d68f\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\tThe components have the same functionalities as in linear regression. However, whereas previously the coefficients of the influencing variables themselves were output, the random forest provides specific information on how strong their influence is on the target variable, i.e. electricity consumption. \t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-7b68918 elementor-widget elementor-widget-image\" data-id=\"7b68918\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t<figure class=\"wp-caption\">\n\t\t\t\t\t\t\t\t\t\t\t<a href=\"https:\/\/hetida.io\/wp-content\/uploads\/2025\/05\/hetida_designer_random_forest_result.webp\" data-elementor-open-lightbox=\"yes\" data-elementor-lightbox-title=\"hetida_designer_random_forest_result\" data-e-action-hash=\"#elementor-action%3Aaction%3Dlightbox%26settings%3DeyJpZCI6NjA3NCwidXJsIjoiaHR0cHM6XC9cL2hldGlkYS5pb1wvd3AtY29udGVudFwvdXBsb2Fkc1wvMjAyNVwvMDVcL2hldGlkYV9kZXNpZ25lcl9yYW5kb21fZm9yZXN0X3Jlc3VsdC53ZWJwIn0%3D\">\n\t\t\t\t\t\t\t<img loading=\"lazy\" decoding=\"async\" width=\"1535\" height=\"424\" src=\"https:\/\/hetida.io\/wp-content\/uploads\/2025\/05\/hetida_designer_random_forest_result.webp\" class=\"attachment-full size-full wp-image-6074\" alt=\"Darstellung des Ergebnisses nach Anwendung der Random-Forest-Methode\" srcset=\"https:\/\/hetida.io\/wp-content\/uploads\/2025\/05\/hetida_designer_random_forest_result.webp 1535w, https:\/\/hetida.io\/wp-content\/uploads\/2025\/05\/hetida_designer_random_forest_result-300x83.webp 300w, https:\/\/hetida.io\/wp-content\/uploads\/2025\/05\/hetida_designer_random_forest_result-1024x283.webp 1024w, https:\/\/hetida.io\/wp-content\/uploads\/2025\/05\/hetida_designer_random_forest_result-768x212.webp 768w\" sizes=\"(max-width: 1535px) 100vw, 1535px\" \/>\t\t\t\t\t\t\t\t<\/a>\n\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\">The results log after executing the above workflow<\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-2f98a1b elementor-widget elementor-widget-text-editor\" data-id=\"2f98a1b\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\tThe day of the week has by far the greatest influence on electricity consumption. However, weekends, school vacations and public holidays also have significant influences that need to be taken into account when making a prediction. The R\u00b2 value is now 0.96 and could therefore be significantly improved by switching from linear regression to the random forest. The random forest is therefore a suitable basis for looking into the future.   \t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-9efdcbe elementor-widget elementor-widget-heading\" data-id=\"9efdcbe\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Result: Forecast for May 25, 2017<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-2f9d6eba elementor-widget elementor-widget-text-editor\" data-id=\"2f9d6eba\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\tIf we zoom in on the week around 25 May in the visualization of the load profile from 2016, we get a familiar view. A five-day week with a constant structure, framed by weekends with lower consumption. \t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-0f0424a elementor-widget elementor-widget-image\" data-id=\"0f0424a\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t<figure class=\"wp-caption\">\n\t\t\t\t\t\t\t\t\t\t\t<a href=\"https:\/\/hetida.io\/wp-content\/uploads\/2025\/05\/hetida_designer_lastgang_mai.webp\" data-elementor-open-lightbox=\"yes\" data-elementor-lightbox-title=\"hetida_designer_lastgang_mai\" data-e-action-hash=\"#elementor-action%3Aaction%3Dlightbox%26settings%3DeyJpZCI6NjA3NiwidXJsIjoiaHR0cHM6XC9cL2hldGlkYS5pb1wvd3AtY29udGVudFwvdXBsb2Fkc1wvMjAyNVwvMDVcL2hldGlkYV9kZXNpZ25lcl9sYXN0Z2FuZ19tYWkud2VicCJ9\">\n\t\t\t\t\t\t\t<img loading=\"lazy\" decoding=\"async\" width=\"2035\" height=\"396\" src=\"https:\/\/hetida.io\/wp-content\/uploads\/2025\/05\/hetida_designer_lastgang_mai.webp\" class=\"attachment-full size-full wp-image-6076\" alt=\"Abbildung der Lastgangdaten\" srcset=\"https:\/\/hetida.io\/wp-content\/uploads\/2025\/05\/hetida_designer_lastgang_mai.webp 2035w, https:\/\/hetida.io\/wp-content\/uploads\/2025\/05\/hetida_designer_lastgang_mai-300x58.webp 300w, https:\/\/hetida.io\/wp-content\/uploads\/2025\/05\/hetida_designer_lastgang_mai-1024x199.webp 1024w, https:\/\/hetida.io\/wp-content\/uploads\/2025\/05\/hetida_designer_lastgang_mai-768x149.webp 768w, https:\/\/hetida.io\/wp-content\/uploads\/2025\/05\/hetida_designer_lastgang_mai-1536x299.webp 1536w\" sizes=\"(max-width: 2035px) 100vw, 2035px\" \/>\t\t\t\t\t\t\t\t<\/a>\n\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\">The results log after executing the above workflow<\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-4c135d2f elementor-widget elementor-widget-text-editor\" data-id=\"4c135d2f\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\tFor the prediction based on the random forest algorithm, we first generate a data set for 2017, in which dependencies on the day of the week, time, public holiday and school vacation are integrated. Now we use the Random Forest Algorithm, trained on the 2016 data, to predict the 2017 data. \t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-8bdd778 elementor-widget elementor-widget-image\" data-id=\"8bdd778\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t<figure class=\"wp-caption\">\n\t\t\t\t\t\t\t\t\t\t\t<a href=\"https:\/\/hetida.io\/wp-content\/uploads\/2025\/05\/hetida_designer_model_vorhersage.webp\" data-elementor-open-lightbox=\"yes\" data-elementor-lightbox-title=\"hetida_designer_model_vorhersage\" data-e-action-hash=\"#elementor-action%3Aaction%3Dlightbox%26settings%3DeyJpZCI6NjA2OCwidXJsIjoiaHR0cHM6XC9cL2hldGlkYS5pb1wvd3AtY29udGVudFwvdXBsb2Fkc1wvMjAyNVwvMDVcL2hldGlkYV9kZXNpZ25lcl9tb2RlbF92b3JoZXJzYWdlLndlYnAifQ%3D%3D\">\n\t\t\t\t\t\t\t<img loading=\"lazy\" decoding=\"async\" width=\"2135\" height=\"895\" src=\"https:\/\/hetida.io\/wp-content\/uploads\/2025\/05\/hetida_designer_model_vorhersage.webp\" class=\"attachment-full size-full wp-image-6068\" alt=\"hetida designer Workflows zur Vorhersage\" srcset=\"https:\/\/hetida.io\/wp-content\/uploads\/2025\/05\/hetida_designer_model_vorhersage.webp 2135w, https:\/\/hetida.io\/wp-content\/uploads\/2025\/05\/hetida_designer_model_vorhersage-300x126.webp 300w, https:\/\/hetida.io\/wp-content\/uploads\/2025\/05\/hetida_designer_model_vorhersage-1024x429.webp 1024w, https:\/\/hetida.io\/wp-content\/uploads\/2025\/05\/hetida_designer_model_vorhersage-768x322.webp 768w, https:\/\/hetida.io\/wp-content\/uploads\/2025\/05\/hetida_designer_model_vorhersage-1536x644.webp 1536w, https:\/\/hetida.io\/wp-content\/uploads\/2025\/05\/hetida_designer_model_vorhersage-2048x859.webp 2048w\" sizes=\"(max-width: 2135px) 100vw, 2135px\" \/>\t\t\t\t\t\t\t\t<\/a>\n\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\">Workflow of the forecast for 2017<\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-48ef2d49 elementor-widget elementor-widget-text-editor\" data-id=\"48ef2d49\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\tAs above, the random forest algorithm is trained on the data for 2016. In parallel, a data set for 2017 is generated in the \u201cTime Data\u201d component, for our company using the input \u201ccountry = CH, province = AG, state = None, year = 2017\u201d. This is then passed to the random forest for prediction.  \t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-21dce64 elementor-widget elementor-widget-text-editor\" data-id=\"21dce64\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>Can we simply adopt the values from 2016 for 2017? A look at the plot of the predicted consumption values shows a different picture! On Thursday, May 25, the predicted consumption drops abruptly. The following Friday shows slightly higher consumption again, but still well below that of a \u201cnormal\u201d Friday.    <em data-renderer-mark=\"true\">What is the reason for this?<\/em><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-a099d21 elementor-widget elementor-widget-image\" data-id=\"a099d21\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t<figure class=\"wp-caption\">\n\t\t\t\t\t\t\t\t\t\t\t<a href=\"https:\/\/hetida.io\/wp-content\/uploads\/2025\/05\/hetida_designer_lastgang_mai.webp\" data-elementor-open-lightbox=\"yes\" data-elementor-lightbox-title=\"hetida_designer_lastgang_mai\" data-e-action-hash=\"#elementor-action%3Aaction%3Dlightbox%26settings%3DeyJpZCI6NjA3NiwidXJsIjoiaHR0cHM6XC9cL2hldGlkYS5pb1wvd3AtY29udGVudFwvdXBsb2Fkc1wvMjAyNVwvMDVcL2hldGlkYV9kZXNpZ25lcl9sYXN0Z2FuZ19tYWkud2VicCJ9\">\n\t\t\t\t\t\t\t<img loading=\"lazy\" decoding=\"async\" width=\"2035\" height=\"396\" src=\"https:\/\/hetida.io\/wp-content\/uploads\/2025\/05\/hetida_designer_lastgang_mai.webp\" class=\"attachment-full size-full wp-image-6076\" alt=\"Abbildung der Lastgangdaten\" srcset=\"https:\/\/hetida.io\/wp-content\/uploads\/2025\/05\/hetida_designer_lastgang_mai.webp 2035w, https:\/\/hetida.io\/wp-content\/uploads\/2025\/05\/hetida_designer_lastgang_mai-300x58.webp 300w, https:\/\/hetida.io\/wp-content\/uploads\/2025\/05\/hetida_designer_lastgang_mai-1024x199.webp 1024w, https:\/\/hetida.io\/wp-content\/uploads\/2025\/05\/hetida_designer_lastgang_mai-768x149.webp 768w, https:\/\/hetida.io\/wp-content\/uploads\/2025\/05\/hetida_designer_lastgang_mai-1536x299.webp 1536w\" sizes=\"(max-width: 2035px) 100vw, 2035px\" \/>\t\t\t\t\t\t\t\t<\/a>\n\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\"><\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-5a94fb9 elementor-widget elementor-widget-text-editor\" data-id=\"5a94fb9\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\tA look at the calendar reveals that May 25, 2017 is a public holiday, Ascension Day. The company&#8217;s machines were at a standstill on this day. Work resumes on the following Friday. However, it can be assumed that many employees take vacation on this bridge day and the company is not running at full capacity, which leads to significantly lower forecast values! The Random Forest algorithm recognized this situation and automatically adjusted its energy forecast accordingly.    \t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-333e344 elementor-widget elementor-widget-text-editor\" data-id=\"333e344\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p><a href=\"https:\/\/hetida.io\/en\/agents-alarms-and-workflows\/\" target=\"_blank\" rel=\"noopener\">Further information on hetida designer, the interactive workflow tool of the hetida platform<\/a><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-1fb92e13 e-flex e-con-boxed e-con e-parent\" data-id=\"1fb92e13\" data-element_type=\"container\" data-e-type=\"container\" 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In addition to the practical question, the main focus is on the possible applications and operation of hetida designer. <\/p>\n","protected":false},"author":7,"featured_media":6054,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"class_list":["post-6088","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-nicht-kategorisiert"],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v27.3 (Yoast SEO v27.3) - https:\/\/yoast.com\/product\/yoast-seo-premium-wordpress\/ -->\n<title>Energy forecasts with hetida designer<\/title>\n<meta name=\"description\" content=\"Using hetida designer, the interactive Python workflow editor of the hetida platform, we are developing a model for forecasting electricity consumption.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" 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