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Cross-temporal aggregation: Improving the forecast accuracy of hierarchical electricity consumption
Forecasting and Strategy Unit, School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece.
School of Management, University of Bath, Bath, United Kingdom.
Lancaster University Management School, Department of Management Science, Lancaster, United Kingdom.ORCID-id: 0000-0003-0211-5218
Forecasting and Strategy Unit, School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece.
2020 (Engelska)Ingår i: Applied Energy, ISSN 0306-2619, E-ISSN 1872-9118, Vol. 261, artikel-id 114339Artikel i tidskrift (Refereegranskat) Published
Abstract [en]

Achieving high accuracy in energy consumption forecasting is critical for improving energy management and planning. However, this requires the selection of appropriate forecasting models, able to capture the individual characteristics of the series to be predicted, which is a task that involves a lot of system and region level, not only the model selection problem is expanded to multiple time series, but we also require aggregation consistency of the forecasts across levels. Although hierarchical forecasting, such as the bottom-up, the top-down, and the optimal reconciliation methods, can address the aggregation consistency concerns, it does not resolve the model selection uncertainty. To address this issue, we rely on Multiple Temporal Aggregation (MTA), which has been shown to mitigate the model selection problem for low-frequency time series. We propose a modification of the Multiple Aggregation Prediction Algorithm, a special implementation of MTA, for high-frequency time series to better handle the undesirable effect of seasonality shrinkage that MTA implies and combine it with conventional cross-sectional hierarchical forecasting. The impact of incorporating temporal aggregation in hierarchical forecasting is empirically assessed using a real data set from five bank branches. We show that the proposed MTA approach, combined with the optimal reconciliation method, demonstrates superior accuracy, aggregation consistency, and reliable automatic forecasting. 

Ort, förlag, år, upplaga, sidor
Elsevier, 2020. Vol. 261, artikel-id 114339
Nyckelord [en]
electricity consumption, exponential smoothing, Hierarchical forecasting, Seasonality shrinkage, Temporal aggregation, Electric power utilization, Energy utilization, Shrinkage, Time series, Electricity-consumption, High frequency time series, Individual characteristics, Low frequency time series, Model selection problem, Seasonality, Forecasting, electricity, energy use, forecasting method, hierarchical system, smoothing, temporal analysis, time series analysis
Nationell ämneskategori
Energisystem Sannolikhetsteori och statistik Energiteknik
Identifikatorer
URN: urn:nbn:se:his:diva-18231DOI: 10.1016/j.apenergy.2019.114339Scopus ID: 2-s2.0-85076830755OAI: oai:DiVA.org:his-18231DiVA, id: diva2:1398736
Tillgänglig från: 2020-02-27 Skapad: 2020-02-27 Senast uppdaterad: 2020-03-02Bibliografiskt granskad

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Kourentzes, Nikolaos

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