Neural network based approach for short-term load forecasting

Short-term load forecast is an essential part of electric power system planning and operation. Forecasted values of system load affect the decisions made for unit commitment and security assessment, which have a direct impact on operational costs and system security. Conventional regression methods are used by most power companies for load forecasting. However, due to the nonlinear relationship between load and factors affecting it, conventional methods are not sufficient enough to provide accurate load forecast or to consider the seasonal variations of load. Conventional ANN-based load forecasting methods deal with 24-hour-ahead load forecasting by using forecasted temperature, which can lead to high forecasting errors in case of rapid temperature changes. This paper presents a new neural network based approach for short-term load forecasting that uses the most correlated weather data for training, validating and testing the neural network. Correlation analysis of weather data determines the input parameters of the neural networks. The suitability of the proposed approach is illustrated through an application to the actual load data of the Egyptian Unified System.

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