Short-term Load Forecasting of an Interconnected Grid by using Neural Network

With the rapid growth of power system and the increase in their complexity of the networks, load forecasting plays a vital role in economic operation of power systems, network planning and infrastructure development. Electricity demand forecasting is concerned with the prediction of a very short term, short term, medium term and long term load demand, depending on the time horizon. This paper presents an application of neural network for real time short term load forecasting and has been compared with the conventional exponential smoothing technique. The daily load data of an inter connected grid Damodar Valley Corporation, operating under Eastern Regional Load Dispatch Centre, India were used as data sets for training and comparing the performance of different neural network topologies along with conventional exponential smoothing technique. The results obtained from Artificial Neural Networks were evaluated with the statistical parameters i.e., Mean Absolute Percentage Error (MAPE) and Mean Absolute Deviation (MAD). MATLAB has been used for simulation, performance and testing the data. Extensive testing shows that neural network based approach has better forecasting accuracy and robustness.