Energy and peak load forecast models using neural network for fast developing area

In electric power systems, energy and peak load forecasting plays an important role in term of policy planning and budget allocation. An accurate forecast method can be helpful in developing a power supply strategy, and development plan, especially for developing countries where the demand is increased with dynamic and high growth rate. This paper proposes energy and peak load forecast models using artificial neural networks (ANN) with multilayer perceptron (MLP) and backpropagation (BP) learning algorithm. This method can forecast the monthly energy and peak load in a year from one month to two years. Factors reflecting weather and economic variation are selected based on correlation coefficients. In addition, a new training technique is introduced. A case study is performed on energy and peak load historical data of Vientiane Municipality system. It demonstrates the accuracy of the proposed method and shows that the forecast model is simple with high accuracy.