Electrical load forecasting through long short term memory

For a power supplier, meeting demand-supply equilibrium is of utmost importance. Electrical energy must be generated according to demand, as large amount electrical cannot stored. the proper functioning supply system, an adequate model for predicting load necessity. In present world, in almost every industry, whether it healthcare, agriculture, and consulting, growing digitization automation prominent feature. As result, sets data related these industries are being generated, which when subjected rigorous analysis, yield out-of-the-box methods optimize business services offered. This paper aims ascertain viability long short term memory (LSTM) neural networks, recurrent network capable handling both long-term short-term dependencies sets, that met by Dispatch Center located major city. The result shows appreciable accuracy forecasting future demand.