A hybrid intelligent approach for the prediction of electricity consumption

Abstract

a b s t r a c tPower load forecasting is an essential tool for energy management systems. Accurate load forecasting supports power companies to make unit commitment decisions and schedule maintenance plans appropriately. In addition to minimizing the power generation costs, it is also important for the reliability of energy systems. This research study presents the implementation of a novel fuzzy wavelet neural network model on an hourly basis, and validates its performance on the prediction of electricity consumption of the power system of the Greek Island of Crete. In the proposed framework, a multiplication wavelet neural network has replaced the classic linear model, which usually appears in the consequent part of a neurofuzzy scheme, while subtractive clustering with the aid of the Expectation-Maximization algorithm is being utilized in the definition of fuzzy rules. The results related to the minimum and maximum load using metered data obtained from the power system of the Greek Island of Crete indicate that the proposed forecasting model provides significantly better forecasts, compared to conventional neural networks models applied on the same dataset.

Neural network approach with teaching–learning-based optimization for modeling and forecasting long-term electric energy demand in Turkey

“…These variables are commonly used in the literature for forecasting EED or EEC [14,25,26,28,29]. Data used in the ANN models were split into two parts: data from years 1980 to 2006 were used for the training procedure, and data from years 2007 to 2012 were used for testing the network.…”

“…For example, Amina et al [26] developed a fuzzy wavelet NN approach to predict the hourly EEC for the Greek island of Crete. Sajadi et al [27] performed an integrated adaptive fuzzy inference model for estimating and forecasting long-term EEC.…”

Performance evaluation of Iranian electricity distribution units by using stochastic data envelopment analysis

“…Also, Omrani et al [18] considered these indicators to evaluate efficiency of Iranian electricity distribution units by using COLS and PCA. Lei and Feng [16] and Amina et al [1] presented forecasting problems in flied of with respect to price and consumption in power generation units.…”

Cyclic electric load forecasting by seasonal SVR with chaotic genetic algorithm

“…However, the training procedure of ANNs models is not only time consuming but also possible to get trapped in local minima and subjectively in selecting the model architecture [33]. Recently, to improve the drawbacks of ANN, many applications of hybrid ANN with statistical methods or other intelligent approaches have received a lot of attentions, such as hybrid with Bayesian inference [34,35], self-organizing map [36,37], wavelet transform [38,39], particle swarm optimization [40], and cooperative co-evolutionary approach [41].…”