Efficient Algorithms for Load Forecasting in Electric Power System Using Artificial Neural Network

— In power systems the next day’s power generation must be scheduled every day, day ahead short-term load forecasting (STLF) is a necessary daily task for power dispatch. Its accuracy affects the economic operation and reliability of the system greatly. Under prediction of STLF leads to insufficient reserve capacity preparation and in turn, increases the operating cost by using expensive peaking units. On the other hand, over prediction of STLF leads to the unnecessarily large reserve capacity, which is also related to high operating cost. The research work in this area is still a challenge to the engineering scholars because of its high complexity. How to estimate the future load with the historical data has remained a difficulty up to now, especially for the load forecasting of holidays, days with extreme weather and other anomalous days. With the recent development of new mathematical, data mining and artificial intelligence tools, it is potentially possible to improve the forecasting result. This paper presents a approach for short-term load forecasting using regression method and artificial neural network. At the end results are compared by correlation analysis Keywords— Short-term, Demand Forecasting, Regression Method , Correlated Data , MATLAB