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Data Consumption-Aware Load Forecasting Scheme for Smart Grid Systems

With the realisation of the Smart Grids (SG) infrastructure at a large scale, demand predictions can be mapped more accurately. Systems base forecasts on user consumption patterns have mined from old feedback systems to modern extensive sensor based network technologies. Therefore, to derive the usage functions, it is essential to perceive such conditions. These directly impact the power needs and the lifestyle of the consumers, and hence, aid in understanding the user behavior and correspondingly manage power generation. This study propounds mean weather conditions as pivotal to determining power usage. Employing a time based data driven approach, this research functions to predict load expectation in various utilization sectors. Repositioning the adoption of climatic conditions from absolute to relative, the investigation establishes the dependence of collective load prediction on the general weather. The simulation results prove that the time dependent load forecasting model accurately predicts the energy demand of residential and commercial sector.