Short-Term Forecasting of Electricity Consumption in Palestine Using Artificial Neural Networks

Nowadays, planning the process of electricity consumption demand is one of the keys success factors for the development of countries. Due to the importance of electricity, countries have greatly paid attention to the prediction of electricity consumption. Electricity consumption prediction is a major problem for the power sector; an efficient prediction will help electrical companies to take the right decisions and to optimize their supply strategies for their work. In this paper, we proposed a model that is used to predict the future electricity consumption depending on the previous consumption. This model provides companies and authorities to know the future information about the electricity consumption, so they can organize their distribution and make suitable plans to maintain the stability in the delivery and distribution of electricity. We aim to create a model that will be able to study the previous electricity consumption patterns and use this data to predict the future electricity consumption. The system analyzes the collected data of electricity consumption of the previous years, then byusing the mean value for each day and the use of Multilayer Feed-Forward with Backpropagation Neural Networks (MFFNNBP) as a tool to predict the future electricity consumption in Palestine. The data used in this paper depends on data collection of months and years. Finally, this proposed model conducts a systematic process with the aim of determining the future electricity consumption in Palestine. The proposed application and the result in this paper are developed in order to contribute to the improvement of the current energy planning tools in Palestine. The experimental results show that the model performs good results of prediction, with low Mean Square Error (MSE).