With increasing of distributed energy resources deployment behind-the-meter and of the power system levels, more attention is being placed on electric load and generation forecasting or prediction for individual residences. While prediction with machine learning based approaches of aggregated power load, at the substation or community levels, has been relatively successful, the problem of prediction of power of individual houses remains a largely open problem. This problem is harder due to the increased variability and uncertainty in user consumption behavior, which make individual residence power traces be more erratic and less predictable. In this paper, we present an investigation of the effectiveness of long short-term memory (LSTM) models to predict individual house power. The investigation looks at hourly (24 h, 6 h, 1 h) and daily (7 days, 1 day) prediction horizons for four different recent datasets. We find that while LSTM models can potentially offer good prediction accuracy for 7 and 1 days ahead for some data sets, these models fail to provide satisfactory prediction accuracies for individual 24 h, 6 h, 1 h horizons.
|Title of host publication||9th International Conference on Renewable Energy Research and Applications, ICRERA 2020|
|Number of pages||5|
|State||Published - Sep 27 2020|
|Event||9th International Conference on Renewable Energy Research and Applications, ICRERA 2020 - Glasgow, United Kingdom|
Duration: Sep 27 2020 → Sep 30 2020
|Name||9th International Conference on Renewable Energy Research and Applications, ICRERA 2020|
|Conference||9th International Conference on Renewable Energy Research and Applications, ICRERA 2020|
|Period||9/27/20 → 9/30/20|
Bibliographical noteFunding Information:
prediction problem, where additional variable measurements, such as temperatures inside and outside, solar irradiance, etc., are included into the input features. ACKNOWLEDGMENT This work was supported by the NSF, grant ECCF 1936494. Any findings and conclusions expressed herein are those of the authors and do not necessarily reflect the views of the NSF.
© 2020 IEEE.
- machine learning
- power load prediction
- residential power load model
ASJC Scopus subject areas
- Electrical and Electronic Engineering
- Industrial and Manufacturing Engineering
- Artificial Intelligence
- Energy Engineering and Power Technology
- Renewable Energy, Sustainability and the Environment