时间序列
自回归积分移动平均
平均绝对百分比误差
能源消耗
人工神经网络
集成学习
系列(地层学)
集合预报
概率预测
消费(社会学)
能量(信号处理)
计量经济学
需求预测
作者
Guoqiang Zhang,Jifeng Guo
出处
期刊:Energy
[Elsevier]
日期:2020-07-15
卷期号:203: 117858-
被引量:13
标识
DOI:10.1016/j.energy.2020.117858
摘要
Abstract In this paper, a novel ensemble method is proposed to forecast the hourly consumption of residential electricity. Firstly, variational mode decomposition (VMD) is applied to decompose weather conditions (relative humidity and temperature, etc.), residential building data (manually operated appliances relevant to residents’ lifestyle, dishwasher, heating heat-pump, and television, etc.), and electricity price into several band-limited intrinsic mode functions (BLIMFs). Then the incremental kernel principal component analysis (IKPCA) is applied to extract the incremental kernel principal components (IKPCs) from the BLIMFs. Next, IKPCs are encoded as images by the Gramian Angular Fields (GAFs). Secondly, a novel ensemble method based on conditional generative adversarial networks (CGANs), is applied to simulate the variability in people’s electrical behavior and weather forecast errors. Moreover, the elitist search strategy of the multi-population genetic algorithm (MPGA) is introduced to realize the communication among each sub-CGAN. And then all sub-CGANs are integrated by the Huffman coding (HC). Thirdly, an improved dragonfly algorithm (IDA) is developed to optimize the weights of HC. The experimental results show that the forecasting results of the proposed ensemble method are obviously better than those of other standard and state-of-the-art methods tested in this paper.
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