Deep learning based approach for short term load forecasting with feature selection

Abstract

The electricity load forecasting is an important task to be carried newlineout in the power system to solve the energy crisis problem. It has become an newlineimportant research area of global concern. It has a prominent role in power newlinesystem operations such as planning the electricity generation, scheduling the newlineelectricity generation, allocating the resources needed for the electricity newlinegeneration, preparing the dispatch scheduling of electricity, making decision newlineon unit commitment, making decision on load increment and decrement, newlinesecure operation of the power generation and maintenance of the power newlinegenerators. It is also important for the reliable and an economic operation of newlinethe power system. Due to the inconvenience of storing the electricity, the newlinepower system cannot be able to generate and store the electricity for a future newlineperiod. The underestimation of the electricity introduces an economical loss newlineto the power system. On the other hand the overestimation creates the wastage newlineof energy. So, the accurate forecasting of the electricity load plays a vital role newlinein the power system. newlineThe accurate load forecasting cannot be easily achieved due to an newlineuncertain and non-linear nature of the electricity. The real time load data newlineconsists of incomplete, irrelevant, redundant data. These irrelevant newlineinformation may misguide the forecasting process or introduce the newlinecomplications to the learning process. So, it becomes the hindrance for newlineachieving the accurate results. newline