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.
Highlights
Medium Term Load Forecasting (MTLF) is extremely important for energy suppliers and other participants in electric energy generation, transmission, distribution and markets
The particle swarm optimization Particle Swarm Optimization (PSO), least square regressive LSR and auto regressive Auto Regressive (AR) methods are presented as MTLF techniques for Jordan electric power systems
This paper has presented approaches used for MTLF of electric loads: LSRM and PSO algorithm
Summary
MTLF is extremely important for energy suppliers and other participants in electric energy generation, transmission, distribution and markets. It helps make decisions, including decisions on purchasing and generating electric power system utilities. Two main challenges have a direct impact on the Jordan power operator center, the first one: obtaining optimal economic dispatch for electrical utilities and the second one is determining medium term unit commitment in order to maintain the system reliability. The existing forecasting predictions of MTLF employed by National Electric Power Company (NEPCO) in Jordan are based on the educated guess assumptions which depend on gathering the electricity consumption of domestic, commercial, industrial, and public lighting sectors. It is necessary to have reliable model to predict the load for medium term periods [5]
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