Yearly Electricity Consumption Forecasting using a Nonhomogeneous Exponential Model Optimized by PSO Algorithm

Abstract

Abstract:Yearly electricity consumption trends of most developing countries usually exhibit approximately exponential growth curves. An optimized nonhomogeneous exponential model (ONEM) is proposed as a method of forecasting electricity consumption by using trend extrapolation. The parameters of the nonhomogeneous exponential equation are obtained by using the inverse accumulated generating operation, discretizing the differential equation, minimizing the residual sum of squares (RSS), and accumulating the homogeneous exponential equation. Furthermore, to improve forecasting precision, particle swarm optimization (PSO) algorithm is used to optimize the equation parameters. To evaluate the forecasting performance for comparison, the said model and two other traditional methods are used to forecast the yearly electricity consumption of India. Empirical results show that this model is much better than traditional methods for each error analysis indicator.

A Small‐Sample Adaptive Hybrid Model for Annual Electricity Consumption Forecasting

“…Furthermore, the parameter of the iteration starting point was obtained by minimizing the RSS. To evaluate forecasting performance, ANN, GM (1,1), and the proposed AHM are used to forecast China's electricity consumption from 1991 to 2014, and then their results are compared. With the forecasting equation of the proposed AHM composed of three constant trend terms, the forecasting results of AHM are less affected by stochastic change than those of ANN.…”

“…By contrast, with the rapid development of the social economy, electricity consumption of developing countries usually changes rapidly and is difficult to forecast [1]. Since 2001, the average yearly growth rate of the top 15 developed electricity consumers of the world is only 1.1%, whereas that of 15 highest electricity consumers in developing nations or countries is as high as 7.4% [2].…”

“…The grey models (GM), which are represented by GM(1, 1), have been successfully used in the trend extrapolation of the annual time series [14][15][16]. The merits of GM (1,1) are in the following two aspects. First advantage is that GM(1, 1) can use small samples to estimate the equation parameters.…”

Research on Power Load Forecasting Model Based on Hybrid Algorithm Optimizing BP Neural Network

“…In the algorithm process, this paper uses the particle swarm optimization algorithm for fast global search to determine the parameters of ant colony and transform the better value into initial information pheromone. Then, using ant colony algorithm for path searching, we put the length of optimal solution, the running time and the numbers of iterations calculated by this set of parameters into the PSO algorithm and update the velocity and position of each particle according to formula until we get the optimal solution for ant colony algorithm [13,14].…”

“…We first set fewer neurons, and gradually increase the number of neurons, until the network training error reaches the expected range. In order to avoid falling into local minimum for neural network, particle swarm and ant colony optimization are introduced to train the weights and threshold for the global optimum [13,14].…”