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R. Holmukhe, “Artificial Neural Network based short term load forecasting technique for Indian power system energy management,” College of Engineering Bhanti dyapeetb Diversity, Pune43, 2007.

has been cited by the following article:

• TITLE: Optimal Scheme with Load Forecasting for Demand Side Management (DSM) in Residential Areas

  AUTHORS: Mohamed AboGaleela, Magdy El-Marsafawy, Mohamed El-Sobki

  KEYWORDS: Component; Demand Side Management(DSM); Load factor(L.F.); Short Term Load Forecatsing(STLF); Long Term Load Forecasting(LTLF); Artificial Neural Network(ANN)

  JOURNAL NAME: Energy and Power Engineering, Vol.5 No.4B, November 11, 2013

  ABSTRACT: Utilities around the world have been considering Demand Side Management (DSM) in their strategic planning. The costs of constructing and operating a new capacity generation unit are increasing everyday as well as Transmission and distribution and land issues for new generation plants, which force the utilities to search for another alternatives without any additional constraints on customers comfort level or quality of delivered product. De can be defined as the selection, planning, and implementation of measures intended to have an influence on the demand or customer-side of the electric meter, either caused directly or stimulated indirectly by the utility. DSM programs are peak clipping, Valley filling, Load shifting, Load building, energy conservation and flexible load shape. The main Target of this paper is to show the relation between DSM and Load Forecasting. Moreover, it highlights on the effect of applying DSM on Forecasted demands and how this affects the planning strategies for utility companies. This target will be clearly illustrated through applying the developed algorithm in this paper on an existing residential compound in Cairo-Egypt.