One-Hour-Ahead Load Forecasting Using Neural Networks

Load forecasting has always been an essential part of an efficient power system's planning and operation. Several electric power companies are now forecasting load power based on conventional methods. However, since the relationship between load power and factors influencing load power is nonlinear, it is difficult to identify its nonlinearity by using conventional methods. Most papers deal with 24-hour-ahead load forecasting or next-day peak load forecasting. These methods forecast the demand power by using forecasted temperature as forecast information. But, when the temperature curves change rapidly on the forecast day, load power changes greatly and forecast error increases. In conventional methods, neural networks use similar-day data to learn the trend of similarity. However, learning of all similar day's data is very complex, and it does not suit learning of neural networks. Therefore, it is necessary to reduce the neural network structure and learning time. To overcome these problems, we propose a one-hour-ahead load forecasting method using the correction of similar-day data. In the proposed prediction method, the forecasted load power is obtained by adding a correction to the selected similar day data.