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• W4313260057 abstract "The long-term demand forecast for annual national electricity and energy consumption plays a vital role in future strategic planning, power system installation programming, energy investment planning, and next-generation unit construction. Three machine learning algorithms of BP-NN, MLR, and LS-SVM were chosen for training forecasting models, with the data on population, GDP, mean temperature, sunshine, rainfall, and frost days in 1993–2019 serving as the input variables. The total data were divided by 70% into the training set (1993–2011) and 30% into the test set (2012–2019), in chronological order. RMSE, MAPE, and MaxError were adopted as the performance criteria. The statistical results show that the gross population of the UK increases year by year from 1993 to 2020. The GDP generally increases before 2007 but has a decline, and then varies with a large amplitude afterward. The electricity and energy consumption of the UK generally increase from 1993 and reach a peak around 2005. Afterward, a decline occurs basically year by year until 2019. The simulation results reveal that all three models predict well on the training set but have some overestimation on the test set. The LS-SVM model has the best forecasting performance among the three models on the training set. The results show that it is feasible to use machine learning algorithms to predict the future electricity and energy consumption of a country based on past economic and livelihood data. In this way, economic decision-makers can rely on the predicted values to make a well-founded layout for future energy construction and investment to avoid waste or a shortage of resources." @default.
• W4313260057 created "2023-01-06" @default.
• W4313260057 creator A5013630392 @default.
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• W4313260057 date "2022-12-25" @default.
• W4313260057 modified "2023-10-05" @default.
• W4313260057 title "Annual Electricity and Energy Consumption Forecasting for the UK Based on Back Propagation Neural Network, Multiple Linear Regression, and Least Square Support Vector Machine" @default.
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• W4313260057 doi "https://doi.org/10.3390/pr11010044" @default.
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