Alexander J. M. Kell, A. Stephen McGough, Matthew Forshaw
arXiv 7 Mar 2021 · Econometrics · publishedSustainable Computing Informatics and Systems (2021)
arXiv:2103.04327 · PDF · DOI · OpenAlex · Extracted main text
Electricity supply must be matched with demand at all times. This helps reduce the chances of issues such as load frequency control and the chances of electricity blackouts. To gain a better understanding of the load that is likely to be required over the next 24h, estimations under uncertainty are needed. This is especially difficult in a decentralized electricity market with many micro-producers which are not under central control. In this paper, we investigate the impact of eleven offline learning and five online learning algorithms to predict the electricity demand profile over the next 24h. We achieve this through integration within the long-term agent-based model, ElecSim. Through the prediction of electricity demand profile over the next 24h, we can simulate the predictions made for a day-ahead market. Once we have made these predictions, we sample from the residual distributions and perturb the electricity market demand using the simulation, ElecSim. This enables us to understand the impact of errors on the long-term dynamics of a decentralized electricity market. We show we can reduce the mean absolute error by 30% using an online algorithm when compared to the best offline algorithm, whilst reducing the required tendered national grid reserve required. This reduction in national grid reserves leads to savings in costs and emissions. We also show that large errors in prediction accuracy have a disproportionate error on investments made over a 17-year time frame, as well as electricity mix.
appendix boundary found by none_found · 100% 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 | A. J. M. Kell, M. Forshaw, A. S. McGough, Long-Term Electricity Mark… (2020) self | 1.000 | 7 | 4 | 100% |
| 2 | A. Kell, A. McGough, M. Forshaw, Segmenting residential smart meter… (2018) | 0.928 | 4 | 4 | 100% |
| 3 | B.-j. Chen, M.-w. Chang, C.-j. Lin, Load Forecasting Using Support V… (2004) 1821–1830 | 0.928 | 4 | 3 | 100% |
| 4 | A. Kell, A. S. Mcgough, M. Forshaw, Segmenting Residential Smart Met… (2018) 91–96 | 0.843 | 3 | 3 | 100% |
| 5 | A. Kell, M. Forshaw, A. S. McGough, ElecSim : Monte-Carlo Open-Sourc… (2019) 556–565 self | 0.811 | 4 | 2 | 100% |
| 6 | National Grid, STOR Market Information Report (October (2019) 0–11 | 0.644 | 2 | 2 | 100% |
| 7 | S.-j. Huang, S. Member, K.-r. Shih, Short-Term Load Forecasting Via… (2003) 673–679 | 0.644 | 2 | 2 | 100% |
| 8 | K.-h. Kim, H.-s. Youn, S. Member, Y.-c. Kang, Short-term load foreca… (2000) https://doi.org/10.1109/59.867141 doi:10.1109/59.867141 | 0.644 | 2 | 2 | 100% |
| 9 | A. K. Singh, Ibraheem, S. Khatoon, M. Muazzam, D. K. Chaturvedi, Loa… (2012) | 0.644 | 2 | 2 | 100% |
| 10 | F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. G… (2011) 2825–2830 | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 53 scored citations.