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• W2087854113 abstract "Forecasting the annual long-term consumption of electrical energy in a country has remained for the Electrical Engineer quite a difficult problem to solve. As an important planning tool, the forecast of electrical energy consumption has to be as precise as possible. The most commonly employed method is that of scenario building. With the scenario method, consumption forecasting is done through the simulation of a sequence of events. The generated data cannot therefore be as stochastic as it is in reality. With the advent of the computer age, numerous other statistical methods for consumption forecasting have been developed. Prominent among them is the forecasting by machine learning with multiple-Input multiple-Output local learning strategy. The objective here is to obtain a forecast which is as precise as possible, while conserving the stochastic nature between the historical and the forecasted data. This article first presents the different strategies for long-term power consumption forecasting using Multiple-Input Multiple-Output local learning strategies. It then proposes, based on the work of earlier researchers, an approach that uses the weighted averages to improve on the level of precision obtained. Furthermore, it applies this new improved calculation method to forecast the power consumption specifically in Cameroon for horizon 2035, when the country aspires to become an emerging economy. The last part of this article utilizes historical data on the electricity consumption of some countries from the World Bank dataset to do a comparative study between the here newly proposed method and that used previously. The results show that, the new method using MISMO plus weighted average delivers more exact results for long-term electrical power consumption forecasts." @default.
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• W2087854113 date "2014-06-30" @default.
• W2087854113 modified "2023-09-26" @default.
• W2087854113 title "Improved Electric Power Demand Forecasting by adapting the Weighted Average to the MISMO Strategy" @default.
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• W2087854113 doi "https://doi.org/10.15676/ijeei.2014.6.2.2" @default.
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