[\/vc_column_text][vc_row_inner css=”.vc_custom_1630262248752{padding-top: 0px !important;padding-bottom: 0px !important;}”][vc_column_inner width=”1\/2″][vc_column_text css=””]mP Xnergy was designed for various forecast requirements from smaller local municipal utilities to large international energy producers, suppliers, network operators and trading companies. It is a sophisticated and universal energy forecast application that meets every need in terms of functionality and cost-performance ratio.[\/vc_column_text][\/vc_column_inner][vc_column_inner width=”1\/2″][vc_column_text]Time series data is used as the basis for all calculations, which are predicted in connection with machine learning algorithms and other methods. The user also has numerous optimization options at their disposal, as well as very powerful options for rule-based forecasting or workflow definition, e.g. for the flow or control of automation processes.[\/vc_column_text][\/vc_column_inner][\/vc_row_inner][\/vc_column][\/vc_row][vc_row gap=”35″ equal_height=”yes” el_class=”loesung-icons-big” css=”.vc_custom_1643972835011{padding-bottom: 0px !important;}”][vc_column width=”1\/3″]\r\n\t\t\t

\r\n\t\t\t\t\r\n\t\t\t\t<\/span>\r\n\t\t\t\tElectricity<\/span>\r\n\t\t\t<\/div>\r\n\t\t[vc_column_text css=””]For optimal demand forecasts for residential, industrial, and commercial customers \u2013 regardless of the profile class, be it HH or NHH meters, or short- or long-term horizons, mP Xnergy includes comprehensive features and prediction capabilities to improve forecast quality, helping e.g., to significantly reduce energy balancing costs.[\/vc_column_text][\/vc_column][vc_column width=”1\/3″]\r\n\t\t\t

\r\n\t\t\t\t\r\n\t\t\t\t<\/span>\r\n\t\t\t\tGas<\/span>\r\n\t\t\t<\/div>\r\n\t\t[vc_column_text css=””]Predicting required gas volumes as accurately as possible is crucial for your economic success. With mP Xnergy, short-, medium- and long-term demand forecasts can be reliably and sustainably produced. Based on various forecasting methods, the aggregated result time series can also be optimized several times a day, and if necessary, adjusted as required.[\/vc_column_text][\/vc_column][vc_column width=”1\/3″]\r\n\t\t\t

\r\n\t\t\t\t\r\n\t\t\t\t<\/span>\r\n\t\t\t\tDistrict Heating<\/span>\r\n\t\t\t<\/div>\r\n\t\t[vc_column_text]Producing reliable load forecasts for district heating generation is not a trivial task. The more accurate the district heating load forecast is, the more efficiently plants and storage facilities can be regulated. Load forecasts thus play an important role in the economics and the efficiency of district heating networks.[\/vc_column_text][\/vc_column][vc_column width=”1\/3″]\r\n\t\t\t

\r\n\t\t\t\t\r\n\t\t\t\t<\/span>\r\n\t\t\t\tSolar Energy<\/span>\r\n\t\t\t<\/div>\r\n\t\t[vc_column_text css=””]A reliable forecast for electricity generation by solar energy (photovoltaic) is the basis for stable power grids and low balancing energy costs. With mP Xnergy, all predictors such as solar irradiation, irradiation angle, cloud cover, temperature, and others, can be included in the forecast models of the generated energy.[\/vc_column_text][\/vc_column][vc_column width=”1\/3″]\r\n\t\t\t

\r\n\t\t\t\t\r\n\t\t\t\t<\/span>\r\n\t\t\t\tWind Energy<\/span>\r\n\t\t\t<\/div>\r\n\t\t[vc_column_text css=””]Wind energy is unquestionably one of the most difficult types of renewable energy to forecast with good accuracy. We would be pleased to show you how you can acquire an optimal forecast result out of your weather, location, and turbine data with the artificial neural networks algorithm used in mP Xnergy.[\/vc_column_text][\/vc_column][vc_column width=”1\/3″]\r\n\t\t\t

\r\n\t\t\t\t\r\n\t\t\t\t<\/span>\r\n\t\t\t\tHydro Power<\/span>\r\n\t\t\t<\/div>\r\n\t\t[vc_column_text css=””]The power generation of run-of-river power plants depends on many influencing factors. In mP Xnergy, the capability exists to process all available information and prediction factors for acquiring an extremely accurate generation forecast together with our algorithms and metaScript Master \u2013 our rule-based forecasting strategy.[\/vc_column_text][\/vc_column][\/vc_row][vc_row css=”.vc_custom_1643972807453{padding-top: 0px !important;padding-bottom: 0px !important;}”][vc_column][vc_column_text]

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