Patel, M.R., Patel, R.V., Dabhi, D. and Patel, J.S. (2019) Long Term Electrical Load Forecasting Considering Temperature Effect Using Multi-Layer Perceptron Neural Network and k-Nearest Neighbor Algorithms. International Journal of Research in Electronics and Computer Engineering, 7, 823-827.

has been cited by the following article:

• TITLE: Electrical Load Forecasting Using Fuzzy System

  AUTHORS: Mahir Faysal, Md. Jahirul Islam, Md. Mohiuddin Murad, Md. Imdadul Islam, M. Ruhul Amin

  KEYWORDS: Defuzzification, Fuzzy Inference, Centroid Method, Surface Plot, APE

  JOURNAL NAME: Journal of Computer and Communications, Vol.7 No.9, September 9, 2019

  ABSTRACT: To cope with the demand and supply of electrical load of an interconnected power system of a country, we need to forecast its demand in advance. In this paper, we use a fuzzy system to forecast electrical load on short-term basis. Here, we consider temperature, humidity, seasons of a year and time segments of a day as the parameters, which govern the demand of electrical load. For each of the parameter, we use several membership functions (MFs) and then apply the Mamdani rule on MFs and the output is determined by using the centroid method. Finally, the surface plot reveals the real scenario of the load demand. The difference between actual load and the output of the fuzzy system is found as +1.65% to -13.76%. The concept of the paper can be applied in interconnected power system of Bangladesh to reduce power loss, especially when generation is higher than the demand.