Abstract
The power system energy management system plays a crucial role in load forecasting. Load forecasting lowers production costs, increases spinning reserve capacity, and improves power system reliability. Financial institutions, power suppliers, and other participants in the electric energy market, such as transmission, generation, and distribution, rely heavily on load forecasts. The allocation of generation for economic reasons is a critical goal of short-term load forecasting. For short-term load forecasting, this research provides a solution paradigm based on an artificial neural network. Dry bulb temperature, Dew point temperature, humidity, and load data are the inputs used to forecast the load. To minimize the error function generated from computed and actual load, the back propagation algorithm was implemented.
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