Load Forecasting of Residential Buildings Based on Deep Learning

Kaile Zhou () and Lulu Wen ()
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Kaile Zhou: Hefei University of Technology
Lulu Wen: Hefei University of Technology

Chapter Chapter 6 in Smart Energy Management, 2022, pp 135-153 from Springer

Abstract: Abstract Accurate electric power load forecasting plays an important role in supporting the power system reliability, promoting the distributed renewable energy integration, and developing effective Demand Response strategies. In this chapter, a deep learning model for the load forecasting with a one-hour resolution of residential buildings is presented. Both model complexity and variability are considered. Hourly-measured residential load data in Austin, Texas, USA are used to demonstrate the effectiveness of the presented model, and the forecasting error was quantitatively evaluated using several metrics. The results showed that the presented model forecasts the aggregated and disaggregated load of residential buildings with higher accuracy compared to conventional methods. Furthermore, the presented deep learning model is also an effective method for filling missing data through learning from history data. It reveals that the presented model has a good learning ability that can accommodate time dependencies to achieve high forecasting accuracy with limited input variables.

Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-981-16-9360-1_6

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DOI: 10.1007/978-981-16-9360-1_6

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