Medium Term Load Forecasting for Jordan Electric Power System Using Particle Swarm Optimization Algorithm Based on Least Square Regression Methods

Abstract

This paper presents a technique for Medium Term Load Forecasting (MTLF) using Particle Swarm Optimization (PSO) algorithm based on Least Squares Regression Methods to forecast the electric loads of the Jordanian grid for year of 2015. Linear, quadratic and exponential forecast models have been examined to perform this study and compared with the Auto Regressive (AR) model. MTLF models were influenced by the weather which should be considered when predicting the future peak load demand in terms of months and weeks. The main contribution for this paper is the conduction of MTLF study for Jordan on weekly and monthly basis using real data obtained from National Electric Power Company NEPCO. This study is aimed to develop practical models and algorithm techniques for MTLF to be used by the operators of Jordan power grid. The results are compared with the actual peak load data to attain minimum percentage error. The value of the forecasted weekly and monthly peak loads obtained from these models is examined using Least Square Error (LSE). Actual reported data from NEPCO are used to analyze the performance of the proposed approach and the results are reported and compared with the results obtained from PSO algorithm and AR model.

Constraint-Aware Electricity Consumption Estimation for Prevention of Overload by Electric Vehicle Charging Station

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An Electric Power Consumption Analysis System for the Installation of Electric Vehicle Charging Stations

“…In order to do this, the optimal values of , , , which are the weights of preprocessed data related to the electric power consumption (charging time and the number of charging) are obtained by the least square method [13,14]. Using these values, the proposed model that minimizes the prediction error is created.…”

“…The least square method determines the weights from the condition that minimizes the sum of the square of the probability error (Equation (4)) [14], which is the difference between the actual and predicted data for the entire data. When the square of the probability error is minimized, the partial derivatives from Equations (5)- (7) are zero.…”