Hierarchical Probabilistic Forecasting of Electricity Demand With Smart Meter Data
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DOI: 10.1080/01621459.2020.1736081
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Cited by:
- Cengiz, Doruk & Tekgüç, Hasan, 2024. "Counterfactual reconciliation: Incorporating aggregation constraints for more accurate causal effect estimates," International Journal of Forecasting, Elsevier, vol. 40(2), pages 564-580.
- Mengchen Zhao & Santiago Gomez-Rosero & Hooman Nouraei & Craig Zych & Miriam A. M. Capretz & Ayan Sadhu, 2024. "Toward Prediction of Energy Consumption Peaks and Timestamping in Commercial Supermarkets Using Deep Learning," Energies, MDPI, vol. 17(7), pages 1-24, April.
- Sakai Ando & Futoshi Narita, 2024. "An Alternative Proof of Minimum Trace Reconciliation," Forecasting, MDPI, vol. 6(2), pages 1-6, June.
- Chen, Zhi & Gaba, Anil & Tsetlin, Ilia & Winkler, Robert L., 2022. "Evaluating quantile forecasts in the M5 uncertainty competition," International Journal of Forecasting, Elsevier, vol. 38(4), pages 1531-1545.
- Olivares, Kin G. & Meetei, O. Nganba & Ma, Ruijun & Reddy, Rohan & Cao, Mengfei & Dicker, Lee, 2024. "Probabilistic hierarchical forecasting with deep Poisson mixtures," International Journal of Forecasting, Elsevier, vol. 40(2), pages 470-489.
- Athanasopoulos, George & Hyndman, Rob J. & Kourentzes, Nikolaos & Panagiotelis, Anastasios, 2024.
"Forecast reconciliation: A review,"
International Journal of Forecasting, Elsevier, vol. 40(2), pages 430-456.
- George Athanasopoulos & Rob J Hyndman & Nikolaos Kourentzes & Anastasios Panagiotelis, 2023. "Forecast Reconciliation: A Review," Monash Econometrics and Business Statistics Working Papers 8/23, Monash University, Department of Econometrics and Business Statistics.
- Jonathan Gumz & Diego Castro Fettermann & Enzo Morosini Frazzon & Mirko Kück, 2022. "Using Industry 4.0’s Big Data and IoT to Perform Feature-Based and Past Data-Based Energy Consumption Predictions," Sustainability, MDPI, vol. 14(20), pages 1-34, October.
- Pesantez, Jorge E. & Li, Binbin & Lee, Christopher & Zhao, Zhizhen & Butala, Mark & Stillwell, Ashlynn S., 2023. "A Comparison Study of Predictive Models for Electricity Demand in a Diverse Urban Environment," Energy, Elsevier, vol. 283(C).
- Leprince, Julien & Madsen, Henrik & Møller, Jan Kloppenborg & Zeiler, Wim, 2023. "Hierarchical learning, forecasting coherent spatio-temporal individual and aggregated building loads," Applied Energy, Elsevier, vol. 348(C).
- Pellini, Elisabetta, 2021. "Estimating income and price elasticities of residential electricity demand with Autometrics," Energy Economics, Elsevier, vol. 101(C).
- Wang, Xiaoqian & Hyndman, Rob J. & Li, Feng & Kang, Yanfei, 2023. "Forecast combinations: An over 50-year review," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1518-1547.
- Di Fonzo, Tommaso & Girolimetto, Daniele, 2023. "Cross-temporal forecast reconciliation: Optimal combination method and heuristic alternatives," International Journal of Forecasting, Elsevier, vol. 39(1), pages 39-57.
- Di Fonzo, Tommaso & Girolimetto, Daniele, 2024. "Forecast combination-based forecast reconciliation: Insights and extensions," International Journal of Forecasting, Elsevier, vol. 40(2), pages 490-514.
- Haben, Stephen & Arora, Siddharth & Giasemidis, Georgios & Voss, Marcus & Vukadinović Greetham, Danica, 2021. "Review of low voltage load forecasting: Methods, applications, and recommendations," Applied Energy, Elsevier, vol. 304(C).
- Corani, Giorgio & Azzimonti, Dario & Rubattu, Nicolò, 2024. "Probabilistic reconciliation of count time series," International Journal of Forecasting, Elsevier, vol. 40(2), pages 457-469.
- Mikkel L. Sørensen & Peter Nystrup & Mathias B. Bjerregård & Jan K. Møller & Peder Bacher & Henrik Madsen, 2023. "Recent developments in multivariate wind and solar power forecasting," Wiley Interdisciplinary Reviews: Energy and Environment, Wiley Blackwell, vol. 12(2), March.
- Park, Jungyeon & Alvarenga, Estêvão & Jeon, Jooyoung & Li, Ran & Petropoulos, Fotios & Kim, Hokyun & Ahn, Kwangwon, 2024. "Probabilistic forecast-based portfolio optimization of electricity demand at low aggregation levels," Applied Energy, Elsevier, vol. 353(PB).
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