Load Forecasting Using Artificial Neural Network : Performance Evaluation with Different Numbers of Hidden Neurons

Short Term Load Forecasting (STLF) is an essential and integral component for energy management and distribution corporations. Accurate load forecasting not only increases the efficiency of a distribution system, but also saves money. STLF is essential in taking quick decisions for a short period such as, for the next few hours or next day or a week. It helps to prevent load shedding and evaluations of various sophisticated financial products on energy pricing. It is important to consider that the forecasting is neither too optimistic nor too conservative because optimistic forecasting creates generation of surplus energy, causing loss of money and resources. Similarly, conservative forecasting will lead to shortage of energy requirement and hence will be a cause for power blackout. This paper examines and analyzes the use of Artificial Neural Network (ANN) as forecasting tools for predicting the future electric load demand and the impact of different numbers of neurons in the hidden layer in a three layered ANN architecture