Forecasting Short-Term Peak Load Demand in the Libyan Power Grid using Multiple Regression Model
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Aspen Hysys, Demand, Electricity, Forecasting, Meteorological, Multiple regression
Abstract
The demand for energy, particularly electricity, has been rising rapidly around the world and plays an important role in socio-economic development, especially in developing countries. Electricity load forecasting is a key task in power plant planning, as well as in efficient operation and sustainable growth of modern electricity distribution networks. This is to make Balancing the relationship between electricity demand and supply. The main purpose of this study is to investigate the impact of meteorological factors such as temperature, relative humidity and wind speed on short-term electricity demand forecasts in Libya. A model has been developed by means of multiple regression analysis and trend method techniques. It was found that there is a good correlation between the factors and electricity use. Since the final model exhibits some little deviation, this approach has proven to be helpful for predicting Libya's electricity consumption. The findings are analyzed and discussed in order to offer recommendations for using this approach for forecasting the short-term demand of electricity.
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