Abstract
Forecasting of electrical load is extremely important for the effective and efficient operation of any power system. Good forecasts results help in minimizing the risk in decision making and reduces the costs of operating the power plant. This work focuses on the short-term load forecast of the 132/33KV transmission sub-station at Port-Harcourt, Nigeria, using the Artificial Neural Network (ANN). It provides accurate week-ahead load forecast using hourly load data of previous weeks. ANN has three sections namely; input, processing and output sections. There are four input parameters for the input section which are historical hourly load data (in MW), time of the day (in hours), days of the week and weekend while the output parameter after the processing (i.e. training, validation and test) is the next week hourly load predicted for the entire system. The technique used is the artificial neural network with the aid of MATLAB software. It was proven to be a good forecast method as it resulted in R-value of 0.988 which gives a mean absolute deviation (MAD) of 0.104 and mean squared error (MSE) of 0.27. Keywords : Load forecast, transmission substation, artificial neural network, power system JEL Classifications: C63, L94, L98, Q48 DOI: https://doi.org/10.32479/ijeep.8629
Highlights
The process of predicting future electric load given historical load and sometimes weather information is known as electricity load forecasting (Samuel et al, 2017)
Load forecasting is very important to the planning and running of electricity companies
This paper focuses on the short-term load forecasting which is used for timely load scheduling and in determining the most economic load dispatch, equipment limitations and operational constraints
Summary
The process of predicting future electric load given historical load and sometimes weather information is known as electricity load forecasting (Samuel et al, 2017). Load forecast is very essential to the entire power sector in order to meet load demands for a given period of time (Samuel et al, 2017; Samuel et al, 2014) It improves the energyefficiency, reliability and effective operation of a power system as it helps in decision-making process and overall security of the system (Feinberg and Genethliou, 2004; Samuel et al, 2016). One of the most prominent transmission substations was considered On this station, short-term load forecast was carried out using daily hourly load readings of the preceding weeks in the month of September, 2017 to predict the following week load demand.
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