Electric Energy Management Modeling for Kingdom of Bahrain

In the deregulated economy, the maximum load forecasting is important for the electric industry. Many applications are included such as the energy generation and purchasing. The aim of the present study is to find the most suitable models for the peak load of the Kingdom of Bahrain. Many mathematical methods have been developing for maximum load forecasting. In the present paper, the modeling of the maximum load, population and GDP (gross domestic product) versus years obtained. The curve fitting technique used to find that models, where Graph 4.4.2 as a tool used to find the models. As well, Neuro-Fuzzy used to find the three models. Therefore, three techniques are used. These three are exponential, linear modeling and Neuro-Fuzzy. It is found that, the Neuro-Fuzzy is the most suitable and realistic one. Then, the linear modeling is the next suitable one.

[1]  Brent R. Young,et al.  Peak Load Shifting with Energy Storage and Price-Based Control System , 2015, Thermal Energy Storage with Phase Change Materials.

[2]  Michael E. Webber,et al.  Modeling peak load reduction and energy consumption enabled by an integrated thermal energy and water storage system for residential air conditioning systems in Austin, Texas , 2015 .

[3]  Mariusz Malinowski,et al.  Comparison of maximum peak power tracking algorithms for a small wind turbine , 2013, Math. Comput. Simul..

[4]  Isa S. Qamber,et al.  Long-term load forecasting for the Kingdom of Bahrain using Monte Carlo method , 2010 .

[5]  Isa S. Qamber.  Annual Maximum Loads Estimation Modeling for Kingdom of Bahrain , 2013 .

[6]  Madan M. Gupta,et al.  On the principles of fuzzy neural networks , 1994 .

[7]  Isa S. Qamber.  Peak Load Modeling for Kingdom of Bahrain , 2012 .

[8]  A. K. Mohamed,et al.  A review of electrical energy management techniques: supply and consumer side (industries) , 2017 .

[9]  June Ho Park,et al.  Comparative Study of Short-Term Electric Load Forecasting , 2014, 2014 5th International Conference on Intelligent Systems, Modelling and Simulation.

[10]  Hassan Soltan,et al.  A methodology for Electric Power Load Forecasting , 2011 .

[11]  N. Tawalbeh,et al.  Peak load evaluation based on the accumulated annual energy , 2012, 2012 16th IEEE Mediterranean Electrotechnical Conference.

[12]  Won-Hwa Hong,et al.  A Study of the Analysis on the Properties of Electricity Peak Load of Large Hotel Buildings in Consideration of Energy Efficiency , 2014 .