Electricity demand estimation using an adaptive neuro-fuzzy network: a case study from the Ontario province Canada
Zahedi, G, Azizi, S, Bahadori, A, Elkamel, A & Wan Alwi, SR in press, 'Electricity demand estimation using an adaptive neuro-fuzzy network: a case study from the Ontario province Canada', Energy, vol. 49, pp. 323-328.
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Electricity is an important asset that influences not only the economy, but political or social security of a country. Reliable and accurate planning and prediction of electricity demand for a country are therefore vital. In this paper, electricity demand in Ontario province of Canada from the year 1976–2005 is modeled by using an (adaptive neuro fuzzy inference system) ANFIS. A neuro fuzzy structure can be defined as an ANN (artificial neural network) which is trained by experimental data to find the parameters of (fuzzy inference system) FIS. Inputs for the model include number of employment, (gross domestic product) GDP, population, dwelling count and two meteorological parameters related to annual weather temperature. The data were collected and screened using statistical methods. Then, based on the data, a neuro-fuzzy model for the electricity demand is built. It was found that electricity demand is most sensitive to employment.