Electricity price forecasting has become an important aspect of promoting competition and safeguarding the interests of participants in electricity market. As market participants, both producers and consumers intent t...Electricity price forecasting has become an important aspect of promoting competition and safeguarding the interests of participants in electricity market. As market participants, both producers and consumers intent to contribute more efforts on developing appropriate price forecasting scheme to maximize their profits. This paper introduces a time series method developed by Box-Jenkins that applies autoregressive integrated moving average (ARIMA) model to address a best-fitted time-domain model based on a time series of historical price data. Using the model’s parameters determined from the stationarized time series of prices, the price forecasts in UK electricity market for 1 step ahead are estimated in the next day and the next week. The most suitable models are selected for them separately after comparing their prediction outcomes. The data of historical prices are obtained from UK three-month Reference Price Data from April 1st to July7th 2010.展开更多
首先采用风险价值(value at risk,VaR)方法根据历史数据计算出各月峰谷时段用电量的历史序列,并在一定的置信度下对谷段和峰段的用电量进行预测;然后采用区间数学方法构建了风险评估模型,以便对供电公司实行峰谷分时电价的风险进行衡量...首先采用风险价值(value at risk,VaR)方法根据历史数据计算出各月峰谷时段用电量的历史序列,并在一定的置信度下对谷段和峰段的用电量进行预测;然后采用区间数学方法构建了风险评估模型,以便对供电公司实行峰谷分时电价的风险进行衡量;最后采用该模型分析了某供电局的历史数据并衡量了其实施峰谷分时电价的风险,结果表明该模型不仅是有效的,而且还可以使需求侧管理更具可操作性。展开更多
文摘Electricity price forecasting has become an important aspect of promoting competition and safeguarding the interests of participants in electricity market. As market participants, both producers and consumers intent to contribute more efforts on developing appropriate price forecasting scheme to maximize their profits. This paper introduces a time series method developed by Box-Jenkins that applies autoregressive integrated moving average (ARIMA) model to address a best-fitted time-domain model based on a time series of historical price data. Using the model’s parameters determined from the stationarized time series of prices, the price forecasts in UK electricity market for 1 step ahead are estimated in the next day and the next week. The most suitable models are selected for them separately after comparing their prediction outcomes. The data of historical prices are obtained from UK three-month Reference Price Data from April 1st to July7th 2010.
文摘首先采用风险价值(value at risk,VaR)方法根据历史数据计算出各月峰谷时段用电量的历史序列,并在一定的置信度下对谷段和峰段的用电量进行预测;然后采用区间数学方法构建了风险评估模型,以便对供电公司实行峰谷分时电价的风险进行衡量;最后采用该模型分析了某供电局的历史数据并衡量了其实施峰谷分时电价的风险,结果表明该模型不仅是有效的,而且还可以使需求侧管理更具可操作性。
