Abstract
Human activities, such as energy consumption and economic development, will significantly affect the natural environment, while changes in the natural environment will also affect the sustainability of human society. Studying the energy consumption changes of human society and forecasting medium and long-term electricity demand will help realize the sustainable development of energy in future society. However, current medium- and long-term electricity consumption forecasts have insufficient data samples and the inability to consider policy impacts. Here, we develop an Economy and Policy Incorporated Computing System (EPICS), which can use artificial intelligence technology to extract the summaries of energy policy texts automatically and calculate the importance index of energy policy. It can also process economic data of different lengths to expand samples of medium- and long-term electricity consumption forecasting effectively. A forecasting method that considers policy factors and mixed-frequency economic data is introduced to estimate future social energy and power consumption. This method has shown good forecasting ability in 27 months. The effect of EPICS can be demonstrated by predicting the medium- and long-term electricity demand.
Highlights
Global warming is one of the main threats to human society, so reducing carbon emissions has become the consensus of all countries
The medium- and long-term electricity consumption forecast in the power industry is the basis for achieving low-carbon power planning and evaluation, which can help the power system achieve economic and low-carbon goals
In order to construct the original policy text as a data set suitable for the Bidirectional Encoder Representation from Transformers (BERT)-based abstract extraction model, we use CoreNLP [28] to segment sentences and pre-process the data set according to the method of See et al [29]
Summary
Global warming is one of the main threats to human society, so reducing carbon emissions has become the consensus of all countries. We propose a medium- and long-term electricity consumption forecasting method integrating economic and policy factors. It takes macroeconomic data of different time scales and the quantified energy policies as input. Aiming at the problem that the traditional medium- and long-term electricity consumption forecasting has insufficient data samples and only considers a single historical load influencing factor [14,15,16,17,18,19], we use the economic and electricity data of 30 provinces in China to expand the data samples, and we take the policy factors and mixed macroeconomic data as the input of the electricity consumption forecasting model to reduce the prediction error of the model. The list of symbols used in this paper is shown in Appendix A
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