Linian Wang, Jianghong Liu, Huibin Zhang, Leye Wang
arXiv 20 May 2024 · Machine Learning
arXiv:2405.14893 · PDF · DOI · OpenAlex · Extracted main text
Accurate day-ahead electricity price forecasting is essential for residential welfare, yet current methods often fall short in forecast accuracy. We observe that commonly used time series models struggle to utilize the prior correlation between price and demand-supply, which, we found, can contribute a lot to a reliable electricity price forecaster. Leveraging this prior, we propose a simple piecewise linear model that significantly enhances forecast accuracy by directly deriving prices from readily forecastable demand-supply values. Experiments in the day-ahead electricity markets of Shanxi province and ISO New England reveal that such forecasts could potentially save residents millions of dollars a year compared to existing methods. Our findings underscore the value of suitably integrating time series modeling with economic prior for enhanced electricity price forecasting accuracy.
appendix boundary found by appendix_titled_section at “Appendix A” · 64% of the source is main text. Read the extracted text to check this.
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 | Nie, Y.; Nguyen, N. H.; Sinthong, P.; and Kalagnanam, J (2022) A time series is worth 64 words: Long-term forecasting with transformers | 0.928 | 4 | 3 | 100% |
| 2 | Zeng, A.; Chen, M.; Zhang, L.; and Xu, Q (2023) Are Transformers Effective for Time Series Forecasting? | 0.843 | 4 | 4 | 75% |
| 3 | Liu, Y.; Hu, T.; Zhang, H.; Wu, H.; Wang, S.; Ma, L.; and Long, M (2023) itransformer: Inverted transformers are effective for time series forecasting | 0.843 | 4 | 4 | 75% |
| 4 | Zhang, Y.; and Yan, J (2023) Crossformer: Transformer utilizing cross-dimension dependency for multivariate time series forecasting | 0.843 | 3 | 3 | 100% |
| 5 | Zheng, K.; Wen, B.; Wang, Y.; and Chen, Q (2020) Impact of electricity price forecasting errors on bidding: a price-taker's perspective | 0.843 | 3 | 3 | 100% |
| 6 | Lago, J.; Marcjasz, G.; De Schutter, B.; and Weron, R (2021) Forecasting day-ahead electricity prices: A review of state-of-the-art algorithms, best practices and an open-access benchmark | 0.737 | 4 | 4 | 50% |
| 7 | Liu, Y.; Li, C.; Wang, J.; and Long, M (2023) Koopa: Learning Non-stationary Time Series Dynamics with Koopman Predictors | 0.737 | 3 | 3 | 67% |
| 8 | Ludwig, N.; Feuerriegel, S.; and Neumann, D (2015) Putting Big Data analytics to work: Feature selection for forecasting electricity prices using the LASSO and random forests | 0.737 | 3 | 3 | 67% |
| 9 | Manfre Jaimes, D.; Zamudio López, M.; Zareipour, H.; and Quashie, M (2023) A Hybrid Model for Multi-Day-Ahead Electricity Price Forecasting considering Price Spikes | 0.737 | 3 | 3 | 67% |
| 10 | Prahara, P. J.; and Hariadi, T. K (2022) Improved Feature Selection Algorithm of Electricity Price Forecasting using SVM | 0.737 | 3 | 3 | 67% |
Showing the top 10 of 57 scored citations.