Jonathan Berrisch, Michał Narajewski, Florian Ziel
arXiv 7 Mar 2022 · Machine Learning · publishedEnergy and AI (2023) · 14 citations (OpenAlex)
arXiv:2203.03342 · PDF · DOI · OpenAlex · Extracted main text
This paper covers predicting high-resolution electricity peak demand features given lower-resolution data. This is a relevant setup as it answers whether limited higher-resolution monitoring helps to estimate future high-resolution peak loads when the high-resolution data is no longer available. That question is particularly interesting for network operators considering replacing high-resolution monitoring predictive models due to economic considerations. We propose models to predict half-hourly minima and maxima of high-resolution (every minute) electricity load data while model inputs are of a lower resolution (30 minutes). We combine predictions of generalized additive models (GAM) and deep artificial neural networks (DNN), which are popular in load forecasting. We extensively analyze the prediction models, including the input parameters' importance, focusing on load, weather, and seasonal effects. The proposed method won a data competition organized by Western Power Distribution, a British distribution network operator. In addition, we provide a rigorous evaluation study that goes beyond the competition frame to analyze the models' robustness. The results show that the proposed methods are superior to the competition benchmark concerning the out-of-sample root mean squared error (RMSE). This holds regarding the competition month and the supplementary evaluation study, which covers an additional eleven months. Overall, our proposed model combination reduces the out-of-sample RMSE by 57.4% compared to the benchmark.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | S. Haben, G. Giasemidis, F. Ziel, S. Arora, Short term load forecast… (2019) 1469–1484 | 0.737 | 3 | 2 | 100% |
| 2 | S. Muzaffar, A. Afshari, Short-term load forecasts using LSTM networ… (2019) 2922–2927 | 0.737 | 3 | 2 | 100% |
| 3 | J. Xie, Y. Chen, T. Hong, T. D. Laing, Relative humidity for load fo… (2016) 191–198 | 0.644 | 2 | 2 | 100% |
| 4 | M. Cai, M. Pipattanasomporn, S. Rahman, Day-ahead building-level loa… (2019) 1078–1088 | 0.511 | 2 | 1 | 100% |
| 5 | J. Lee, Y. Cho, National-scale electricity peak load forecasting: Tr… (2022) 122366 | 0.511 | 2 | 1 | 100% |
| 6 | S. Wood, Generalized Additive Models: An Introduction with R, 2nd Ed… (2017) | 0.511 | 2 | 1 | 100% |
| 7 | H. Acaroglu, F. P. Garcá Márquez, Comprehensive review on electricit… (2021) 7473 | 0.405 | 1 | 1 | 100% |
| 8 | E. Aguilar Madrid, N. Antonio, Short-term electricity load forecasti… (2021) 50 | 0.405 | 1 | 1 | 100% |
| 9 | T. Akiba, S. Sano, T. Yanase, T. Ohta, M. Koyama, Optuna: A next-gen… (2019) pp | 0.405 | 1 | 1 | 100% |
| 10 | K. Amarasinghe, D. L. Marino, M. Manic, Deep neural networks for ene… (2017) IEEE 26th International Symposium on Industrial Electronics (ISIE), IEEE, 2017, pp | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 46 scored citations.