Short term load forecasting as a base core of smart grid integrated intelligent energy management system

The energy sector is one of the major sectors where big steps can be taken towards a sustainable future. This paper represents a study to determine the most effective and accurate technique for Short Term Load Forecasting (STLF) that can be done through Integrated Intelligent Energy Management processes for Smart Grid. The Load Forecasting shall be based on the historical data only. This study compared the results of 56 different scenarios in the number of inputs, different algorithms, and input method. ANN, PSO, MAACPSO, and ANFIS were used as different algorithms. Different input methods include a new proposed method which considers Multi Stages Forecasting. Study verification is done by testing versus other practical STLF methods using traditional inputs. The proposed method results are auspicious with reduced Mean Absolute Error.