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Erschienen in:
2018 | OriginalPaper | Buchkapitel
Impacts of Climate Change and Socioeconomic Development on Electric Load in California
verfasst von : Jie Shi, Nanpeng Yu
Erschienen in: AI 2018: Advances in Artificial Intelligence
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Abstract
In order to develop policies to mitigate the impacts of climate change on energy consumption, it is imperative to understand and quantify the impacts of climate change and socioeconomic development on residential electric load. This paper develops a feed-forward neural network to model the complex relationships among socioeconomic factors, weather, distributed renewable generation, and electric load at the census block group level. The influence of different explanatory variables on electric load is quantified through the layer-wise relevance propagation method. A case study with 4,000 census block groups in southern California is conducted. The results show that temperature, housing units, and solar PV systems have the highest influence on net electric load. The scenario analysis reveals that net electric load of disadvantaged communities are much more sensitive to rising temperature than the non-disadvantaged ones. Hence, they are much more vulnerable to climate change.