A Simple Regression Model for Electrical Energy Forecasting

Abstract

Long term load forecasting is an important aspect of electric utility resource planning and utility expansion. This paper presents a simple regression analysis based model involving population and per capita GDP for long term forecasting of India's sector-wise electrical energy demand. The model requires an input, the year of the forecast, and predicts the sector-wise energy demand. Sector-wise energy consumption during the years 1990 -2012 forms the data for developing the forecasting model. It presents the forecasted sector-wise energy demands up to the 2025.

Medium term electrical load forecast of Abuja Municipal Area council using artificial neural network method

“…In multiple linear regression, more than one independent variables or function of variables are present. The regression model for a set of data having m number of independent variables is as given [17] in eq. 14;…”

“…The accuracy and performance of regression models on the form of data generating process and its relation to the regression approach employed, with the best fit was obtained using the least squares method [17]. Since this work has more than one independent variable, the multiple linear regression model was applied in carrying out the load forecasting.…”

Long-term Sector-wise Electrical Energy Forecasting Using Artificial Neural Network and Biogeography-based Optimization

“…The number of neurons in the hidden layer is varied from 2 to 10 for the ANNBP, ANNHSA, and PM, and the resulting MAPEs Tables 2, 3, and 4, respectively, for the ANNBP, ANNHSA, and PM. The detailed results of RM are available in [5]. Analyzing the tables, it is clear that the sector-wise energy demands given by the PM are in general lower than those of the other three methods.…”

“…It has been tailored to carry the advantages of being easy to implement and simple to understand the relationship between input and output variables [4]. A simple RM with a minimum number of inputs for long-term forecasting of sector-wise electrical energy demand was given in [5].…”

Systems monitoring based on robust estimation of stochastic time series models

“…The processes occurring in many subject areas can be adequately described using stochastic autoregressive models АR(p) [1] The use of relation (1) in the form of a diagnostic model makes it possible to solve the problems of monitoring, flaw detection, fault detection, quality control, and defect prevention [1][2][3]. When the system is operational, the values of the parameters of the autoregressive model (1) remain stable and correspond to the normal operation range.…”