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A Stochastic Estimation Framework for Yearly Evolution of Worldwide Electricity Consumption

机译:全球电力消耗年迈的随机估计框架

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The determination of electric energy consumption is remarked as one of the most vital objectives for electrical engineers as it is highly essential in determining the actual energy demand made on the existing electricity supply. Therefore, it is important to find out about the increasing trend in electric energy demands and use all over the world. In this work, we present a prediction scheme for the progression of worldwide aggregates of cumulative electricity consumption using the time series of the records released annually for the net electricity use throughout the world. Consequently, we make use of an autoregressive (AR) model by retaining the best possible autoregression order recording the highest regression accuracy and the lowest standardized regression error. The resultant regression scheme was proficiently employed to regress and forecast the evolution of next-decade data for the net consumption of electricity worldwide from 1980 to 2019 (in billion kilowatt-hours). The experimental outcomes exhibited that the highest accuracy in regressing and forecasting the global consumption of electricity is 95.7%. The prediction results disclose a linearly growing trend in the amount of electricity issued annually over the past four decades’ observation for the global net electricity consumption dataset.
机译:电能消耗的确定被评为作为电气工程师最重要的目标之一,因为确定对现有电力供应的实际能量需求非常重要。因此,重要的是要了解电能需求的增加趋势,并在世界各地使用。在这项工作中,我们使用每年为全球净电力使用的记录的时间序列,为全球累计电力消费的累计汇总的进展提出了预测方案。因此,我们通过保留最佳的回归准确度和最低标准化回归误差来利用自回归(AR)模型。由此产生的回归方案熟悉,从1980年到2019年(十亿千瓦时),迈出了回归和预测全球净电力净消费的进展情况。实验结果表明,回归和预测电力消耗的最高准确性为95.7%。预测结果在过去的四十年对全球净电力消费数据集的观察中透露了每年发布的电量的线性增长趋势。

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