Jordan is a country with highly fossil fuel deficiency and thus other energy sources are needed to be explored. Solar energy in Jordan is highly recognized as a good source of energy and an excellent substitute to the...Jordan is a country with highly fossil fuel deficiency and thus other energy sources are needed to be explored. Solar energy in Jordan is highly recognized as a good source of energy and an excellent substitute to the fossil fuel. The solar energy in this article is obtained via data bases and modeling techniques for the specified place coordinate and angle of inclination. The angles of sun irradiations are different throughout the year;therefore solar energy needs to be magnified by optimizing the angle of inclination of solar cells. In this research, the optimized angles throughout the year are obtained to be in the range: 10°-60°. Solar energy can serve the residential building, the findings of this research show that every 1 m2 of the solar cell may contribute to about 60%-70% of customer needs of electricity throughout the year. The application of solar energy concept in the design of building will play an important role in energy sustainability.展开更多
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 ...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.展开更多
文摘Jordan is a country with highly fossil fuel deficiency and thus other energy sources are needed to be explored. Solar energy in Jordan is highly recognized as a good source of energy and an excellent substitute to the fossil fuel. The solar energy in this article is obtained via data bases and modeling techniques for the specified place coordinate and angle of inclination. The angles of sun irradiations are different throughout the year;therefore solar energy needs to be magnified by optimizing the angle of inclination of solar cells. In this research, the optimized angles throughout the year are obtained to be in the range: 10°-60°. Solar energy can serve the residential building, the findings of this research show that every 1 m2 of the solar cell may contribute to about 60%-70% of customer needs of electricity throughout the year. The application of solar energy concept in the design of building will play an important role in energy sustainability.
文摘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.
