Engineering

Statistics

Support vector machine

Computer science

Regression analysis

Regression

Mathematics

Econometrics

Least squares support vector machine

Sociology

Artificial intelligence

Consumption (sociology)

Artificial neural network

Electricity

Machine learning

Electrical engineering

Social science

International Journal of Electrical Power & Energy Systems

Accurate electricity consumption forecast has primary importance in the energy planning of the developing countries. During the last decade several new techniques are being used for electricity consumption planning to accurately predict the future electricity consumption needs. Support vector machines (SVMs) and least squares support vector machines (LS-SVMs) are new techniques being adopted for energy consumption forecasting. In this study, the LS-SVM is implemented for the prediction of electricity energy consumption of Turkey. In addition to the traditional regression analysis and artificial neural networks (ANNs) are considered. In the models, gross electricity generation, installed capacity, total subscribership and population are used as independent variables using historical data from 1970 to 2009. Forecasting results are compared using diverse performance criteria in this study with each other. Receiver operating characteristic (ROC) analysis is realized for determining the specificity and sensitivity of the empirical results. The results indicate that the proposed LS-SVM model is an accurate and a quick prediction method.

Forecasting electricity consumption: A comparison of regression analysis, neural networks and least squares support vector machines