Neural Network (NN) based Demand Side Management (DSM)

Demand Side Management (DSM) programs consist of the scheduling, planning, executing and monitoring activities of load demand in such a way to ensure the load side management at utilities as well as consumers´ levels. The main objective of DSM is optimizing the utilization of available electricity. In developing countries like Pakistan the importance of DSM has increased significantly due to considerable gap in supply and demand of electricity. With effective use of DSM, this supply-demand gap can be minimized to extensive limits. For this purpose one of the power distribution companies of Pakistan Electric Power Company (PEPCO) named as Gujranwala Electric Power Company (GEPCO) located in the hub of industrial and commercial zone and also suffering from huge supply-demand gap has been selected as a case study. The complete data in the form of load curves spread over the period of May 2009-April 2010 has been taken from Regional Control Centre (RCC) of GEPCO. Various DSM techniques have been applied keeping in view the requirement of solution of supply-demand gap and to increase the efficient use of electricity. Literature review reveals that the innovative approach presented in this paper based on Feed Forward Neural Network will really help the energy stake sector holders to overcome this world wide issue. The reason of choosing Neural Network (NN) among the other Artificial Intelligence (AI) techniques is that the Neural Networks are more suitable for recognition of patterns that consists of crisp mathematical combinations. Proposed innovative technique has been implemented successfully by using the Neural Network toolbox of MATLAB to implement DSM practically which results in a complete plan/schedule to get the desired output. Final results have been shown graphically for better understanding in the form of load curves.