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Implementing Very-Short-Term Forecasting of Residential Load Demand Using a Deep Neural Network Architecture

Author

Listed:
  • Reynaldo Gonzalez

    (Department of Electrical and Computer Engineering, University of Texas at San Antonio, San Antonio, TX 78249, USA)

  • Sara Ahmed

    (Department of Electrical and Computer Engineering, University of Texas at San Antonio, San Antonio, TX 78249, USA)

  • Miltiadis Alamaniotis

    (Department of Electrical and Computer Engineering, University of Texas at San Antonio, San Antonio, TX 78249, USA)

Abstract

The need for and interest in very-short-term load forecasting (VSTLF) is increasing and important for goals such as energy pricing markets. There is greater challenge in predicting load consumption for residential-load-type data, which is highly variable in nature and does not form visible patterns present in aggregated nodal-type load data. Previous works have used methods such as LSTM and CNN for VSTLF; however, the use of DNN has yet to be investigated. Furthermore, DNNs have been effectively used in STLF but have not been applied to very-short-term time frames. In this work, a deep network architecture is proposed and applied to very-short-term forecasting of residential load patterns that exhibit high variability and abrupt changes. The method extends previous work by including delayed load demand as an input, as well as working for 1 min data resolution. The deep model is trained on the load demand data of selected days—one, two, and a week—prior to the targeted day. Test results on real-world residential load patterns encompassing a set of 32 days (a sample from different seasons and special days) exhibit the efficiency of the deep network in providing high-accuracy residential forecasts, as measured with three different error metrics, namely MSE, RMSE, and MAPE. On average, MSE and RMSE are lower than 0.51 kW and 0.69 kW, and MAPE lower than 0.51%.

Suggested Citation

  • Reynaldo Gonzalez & Sara Ahmed & Miltiadis Alamaniotis, 2023. "Implementing Very-Short-Term Forecasting of Residential Load Demand Using a Deep Neural Network Architecture," Energies, MDPI, vol. 16(9), pages 1-16, April.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:9:p:3636-:d:1130950
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    References listed on IDEAS

    as
    1. Tian Shi & Fei Mei & Jixiang Lu & Jinjun Lu & Yi Pan & Cheng Zhou & Jianzhang Wu & Jianyong Zheng, 2019. "Phase Space Reconstruction Algorithm and Deep Learning-Based Very Short-Term Bus Load Forecasting," Energies, MDPI, vol. 12(22), pages 1-17, November.
    2. Sha, Huajing & Xu, Peng & Lin, Meishun & Peng, Chen & Dou, Qiang, 2021. "Development of a multi-granularity energy forecasting toolkit for demand response baseline calculation," Applied Energy, Elsevier, vol. 289(C).
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