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Short-Term Load Forecasting using Seasonal Artificial Neural Network

This paper presents a Seasonal Artificial Neural Network (SANN) approach in forecasting the short-term load for the National Control Centre (NCC) Oshogbo of the Power Holding Company of Nigeria (PHCN) Limited. SANNs are sets of ANNs that are trained with data for particular seasonal and hourly time durations. Unlike the conventional ANN configuration, the networks in SANNs are entirely independent of each other with each network fully dedicated to data on similar features for a specific duration. The proposed SANNs consist of four modules. Each module is assigned to a particular season, and includes three ANNs, each of which is fully dedicated to a particular time period. The time periods include 00:00 – 08:00, 09:00 – 15:00, and 16:00 – 24:00. Three types of variables are used as inputs to the neural networks: (1) time-related input, (2) electric load-related inputs, and (3) national and religious event input. Data from the year 2006 to 2009 were used as test cases. By applying the SANN good results were obtained.