Application of Gray Expansion Model in Energy Economic Analysis and Load Forecasting

Yin Jie
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Abstract

Objective and effective prediction of energy consumption can not only optimize the energy consumption structure, but also provide important information for the government to formulate energy conservation and emission reduction measures. With the development of new energy sources and changes in the global energy consumption structure, historical energy data that are too old may no longer be reliable for forecasting, which leads to a decrease in the amount of information on energy, and the gray theory, which is applicable to “poor information”, has gained attention. Firstly, the optimization of energy economic objectives and transformation path methods at this stage is clarified; then, the DEA-Malmqusit model is used to improve the shortcomings of the traditional model that can only compare different cross-sections at the same time node, and to evaluate and analyze the full-factor multi-indicators of energy enterprises in terms of technological empowerment, environmental dynamics, and economic output efficiency; finally, the LEAPS-based energy system consumption and load capacity prediction model. The results show that the traditional algorithm is not accurate enough and has some deviation when the energy raw data fluctuates a lot. The algorithm proposed in this paper still gives a better prediction, predicting a city’s carbon emission to be 65,240,100 tons in 2024, with a 3.6% increase in energy output year by year.
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灰色扩展模型在能源经济分析和负荷预测中的应用
客观有效的能源消费预测不仅可以优化能源消费结构,还可以为政府制定节能减排措施提供重要信息。随着新能源的发展和全球能源消费结构的变化,过于陈旧的能源历史数据可能不再是预测的可靠依据,这导致能源信息量的减少,适用于 "信息贫乏 "的灰色理论受到关注。首先,明确了现阶段能源经济目标的优化和转型路径方法;然后,利用DEA-Malmqusit模型改进了传统模型只能在同一时间节点对不同截面进行比较的缺点,从技术赋权、环境动态、经济产出效率等方面对能源企业进行全要素多指标评价分析;最后,提出了基于LEAPS的能源系统消费和负荷能力预测模型。结果表明,当能源原始数据波动较大时,传统算法不够准确,存在一定偏差。本文提出的算法仍然给出了较好的预测结果,预测 2024 年某市的碳排放量为 6524.01 万吨,能源产出逐年增长 3.6%。
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来源期刊
Strategic Planning for Energy and the Environment
Strategic Planning for Energy and the Environment Environmental Science-Environmental Science (all)
CiteScore
1.50
自引率
0.00%
发文量
25
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