DEVELOPMENT OF SHORT-TERM LOAD FORECASTING SYSTEM FOR DEMAND SITE MANAGEMENT OF TRANS-EKULU 33/11KVA DISTRIBUTIVE TRANSFORMER USING ARTIFICIAL INTELLIGENCE

This paper presents the development of an artificial intelligence-based short-term load forecasting system for demand site management of the Trans-Ekulu 33/11KV, 15MVA distributive transformer. Empirical data collection and analysis were performed using Newton Raphson load flow analysis to study feeder performance, and the amount of generated power on a short-term basis (24 hours) was estimated using a unit cost of 5.22 naira per kW. The collected data was used to train an artificial neural network-based load forecasting system, which was then implemented using Simulink. The system was tested through simulation, and the results showed that the algorithm was able to accurately estimate the behavior of the transformer in the next 24 hours, with a regression of 0.9989. The load forecasting system was integrated into the EEDC center for managing the Trans-Ekulu feeder, and the results showed that the behavior of the feeder was correctly estimated by the forecasting system