Ankitha Nandipura Prasanna, Priscila Grecov, Angela Dieyu Weng, Christoph Bergmeir
arXiv 19 Sep 2022 · Machine Learning · publishedIEEE Transactions on Power Systems (2023) · 3 citations (OpenAlex)
arXiv:2209.08885 · PDF · DOI · OpenAlex · Extracted main text
The electricity industry is heavily implementing smart grid technologies to improve reliability, availability, security, and efficiency. This implementation needs technological advancements, the development of standards and regulations, as well as testing and planning. Smart grid load forecasting and management are critical for reducing demand volatility and improving the market mechanism that connects generators, distributors, and retailers. During policy implementations or external interventions, it is necessary to analyse the uncertainty of their impact on the electricity demand to enable a more accurate response of the system to fluctuating demand. This paper analyses the uncertainties of external intervention impacts on electricity demand. It implements a framework that combines probabilistic and global forecasting models using a deep learning approach to estimate the causal impact distribution of an intervention. The causal effect is assessed by predicting the counterfactual distribution outcome for the affected instances and then contrasting it to the real outcomes. We consider the impact of Covid-19 lockdowns on energy usage as a case study to evaluate the non-uniform effect of this intervention on the electricity demand distribution. We could show that during the initial lockdowns in Australia and some European countries, there was often a more significant decrease in the troughs than in the peaks, while the mean remained almost unaffected.
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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 | D. Salinas, V. Flunkert, J. Gasthaus, and T. Januschowski, “DeepAR:… (2020) DeepAR: Probabilistic forecasting with autoregressive recurrent networks | 1.000 | 5 | 3 | 100% |
| 2 | E. Buechler, S. Powell, T. Sun, C. Zanocco, N. Astier, J. Bolorinos,… (2020) Power and the pandemic: Exploring global changes in electricity demand during Covid-19 | 0.811 | 4 | 2 | 100% |
| 3 | P. Grecov, A. N. Prasanna, K. Ackermann, S. Campbell, D. Scott, D. I… (2022) Probabilistic causal effect estimation with global neural network forecasting models | 0.737 | 3 | 2 | 100% |
| 4 | P. Grecov, K. Bandara, C. Bergmeir, K. Ackermann, S. Campbell, D. Sc… (2021) Causal inference using global forecasting models for counterfactual prediction | 0.737 | 3 | 2 | 100% |
| 5 | S. M. Mazhari, N. Safari, C. Chung, and I. Kamwa, “A quantile regres… (2018) A quantile regression-based approach for online probabilistic prediction of unstable groups of coherent generators in power syst… | 0.737 | 3 | 2 | 100% |
| 6 | C. Wan, J. Lin, J. Wang, Y. Song, and Z. Y. Dong, “Direct quantile r… (2016) Direct quantile regression for nonparametric probabilistic forecasting of wind power generation | 0.644 | 2 | 2 | 100% |
| 7 | Y. Wen, D. AlHakeem, P. Mandal, S. Chakraborty, Y.-K. Wu, T. Senjyu,… (2019) Performance evaluation of probabilistic methods based on bootstrap and quantile regression to quantify pv power point forecast u… | 0.644 | 2 | 2 | 100% |
| 8 | D. W. Van der Meer, M. Shepero, A. Svensson, J. Widén, and J. Munkha… (2018) Probabilistic forecasting of electricity consumption, photovoltaic power generation and net demand of an individual building usi… | 0.585 | 3 | 1 | 100% |
| 9 | J. Abrell, M. Kosch, and S. Rausch, “How effective is carbon pricing… (2022) How effective is carbon pricing?—A machine learning approach to policy evaluation | 0.511 | 2 | 1 | 100% |
| 10 | C. Graf, F. Quaglia, and F. A. Wolak, “(Machine) learning from the C… (2021) (Machine) learning from the Covid-19 lockdown about electricity market performance with a large share of renewables | 0.511 | 2 | 1 | 100% |
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