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Adequacy of neural networks for wide-scale day-ahead load forecasts on buildings and distribution systems using smart meter data

Valgaev, Oleg; Kupzog, Friederich; Schmeck, Hartmut

Abstract:
Power system operation increasingly relies on numerous day-ahead forecasts of local, disaggregated loads such as single buildings, microgrids and small distribution system areas. Various data-driven models can be effective predicting specific time series one-step-ahead. The aim of this work is to investigate the adequacy of neural network methodology for predicting the entire load curve day-ahead and evaluate its performance for a wide-scale application on local loads. To do so, we adopt networks from other short-term load forecasting problems for the multi-step prediction. We evaluate various feed-forward and recurrent neural network architectures drawing statistically relevant conclusions on a large sample of residential buildings. Our results suggest that neural network methodology might be ill-chosen when we predict numerous loads of different characteristics while manual setup is not possible. This article urges to consider other techniques that aim to substitute standardized load profiles using wide-scale smart meters data.


Verlagsausgabe §
DOI: 10.5445/IR/1000135267
Veröffentlicht am 17.07.2021
Originalveröffentlichung
DOI: 10.1186/s42162-020-00132-6
Scopus
Zitationen: 3
Dimensions
Zitationen: 2
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Angewandte Informatik und Formale Beschreibungsverfahren (AIFB)
Karlsruher Institut für Technologie (KIT)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2020
Sprache Englisch
Identifikator ISSN: 2520-8942
KITopen-ID: 1000135267
Erschienen in Energy Informatics
Verlag SpringerOpen
Band 3
Heft 1
Seiten 28
Nachgewiesen in Dimensions
Scopus
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