Growing electricity needs, limited resources and absence of storing electricity force sectoral players to prepare various action plans and safety precautions. Requirement to consume electricity right after production does not impose to provide supply-demand balance for market actors.
Purpose of this study is to develop a time series model forecasting energy demand. It is possible to forecast short, mid and long term energy demands by using statistical methods and artificial intelligence algorithms.
Weather condition, seasonality, gdp, population, import, export, building area and number of tools data are used as input for forecasting energy demand.
Energy Production Forecast
Inability to store electricity raises the importance of highly accurate planning of electricity production balance and forecast. Accuracy of forecasts will also increase effectiveness of planning.
Forecasting mistakes cause unbalanced supply demands which affects operational cost, reliability and efficiency negatively. Therefore, forecasting accuracy has a great importance.
Strong inferences may be acquired by minimizing forecasting mistakes with statistical methods and artificial algorithms. Forecasting energy production is mainly based on meterological data such as solar exposure and temperature.
Pricing Rules Management
We are handling complicated agreement pricing demands with IBM’s leader rule engine Rule Manager. You may trust pricing decisions made by your business analyst whether you are our retail customer or an industrial corporation.
Dynamic pricing, segment-based pricing, hour-based pricing and consumption segmentation may be easily adjusted and integrated to SAP platform with JPricing for Utilities solution.