Abstract
The main aim of this paper is to model the industrial power consumption in Nigeria with the Adaptive Neuro-Fuzzy Inference System (ANFIS) model and then forecast the industrial power consumed for the next five years beyond the available data. About 45 years (1970 to 2015) dataset was obtained from the Central Bank of Nigeria (CBN), the National Bureau of Statistics (NBS) and other relevant organizations. The data includes population, rainfall, electricity connectivity and temperature which are the explanatory variables. Matlab was used along with the dataset to train and evaluate the ANFIS model which was then used to forecast the industrial power consumption in Nigeria for the years 2016 to 2020.The prediction performance of the ANFIS model was compared to those of Autoregressive Moving Average model and Moving Average model. From the result obtained, ANFIS gave R-square value of 0.9977 (99.77%), SSE value of 395.3674 and RMSE value of 2.9641. The regression coefficient of 99.77% shows that about 99.77% of the variations in the industrial power consumption in Nigeria for the years 1970 to 2015 are explained by the selected explanatory variables. The forecast result showed that the Nigerian industrial power consumption would be about 374.7 MW at the end of 2020 which is about 73.1% increase from the industrial power consumption in 2015. As such, based on the industrial power consumption in 2015, over 73% increment in power supply to the industrial sector will be required to satisfy the industrial sector's power demand in 2020.
Highlights
Forecasting is the act of making prediction of future events and situations
The forecast result showed that the Nigerian industrial power consumption would be about 374.7 MW at the end of 2020 which is about 73.1%
It allows power utilities companies to plan their operations such as unit commitment and generator maintenance beforehand, and serve their customers with more reliable and more economically efficient electric power [4, 5, 6, 7]. This has become important seeing that energy resources are often limited along with challenges emanating from environmental factors [8, 9, 10, 11, 12]
Summary
Forecasting is the act of making prediction of future events and situations. In different areas of life, forecasting is the basic technique of decision making targeted at minimizing risk in decision making and reducing unanticipated cost [1, 2, 3]. In the power industry, correct prediction of future load demand enables power utility companies to supply electrical energy to the consumers economically. It allows power utilities companies to plan their operations such as unit commitment and generator maintenance beforehand, and serve their customers with more reliable and more economically efficient electric power [4, 5, 6, 7]. This has become important seeing that energy resources are often limited along with challenges emanating from environmental factors [8, 9, 10, 11, 12]. Studies have shown that geographical location, population, social factors and weather factors have different impacts on power systems [11, 12, 13, 14, 15, 16].accurate load forecasting model that takes into consideration the essential factors that do affect the power demand pattern is important in power system planning
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