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Conference Papers Year : 2020

Comparing high accurate regression models for short-term load forecasting in smart buildings

Abstract

Load prediction is at the core of many smart grid topics because its accuracy affects the resilience and optimal operation of the power grid. Short term load forecasting (STLF) specific to buildings is needed for both demand and production sides management, to help reducing peak electricity demand, optimizing energy planning, management, and conservation among other. In this paper, we analyze and compare five of the best state of art performing regressive STLF algorithms with deep learning techniques. These methods are compared in the same context with the same real (large) data-set, leading to a fair comparison and precise evaluation. The load forecasting is deployed with and without taking into account environmental features. The idea is to help determine which one could be considered in the optimization loop of an energy management systems, either at the level of a district (local energy communities, or smart districts) or directly at the level of individual households (smart residential buildings).
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Dates and versions

hal-03016192 , version 1 (20-11-2020)

Identifiers

  • HAL Id : hal-03016192 , version 1

Cite

T T Q Nguyen, T P T Tran, Vincent Debusschere, Christophe Bobineau, Rémy Rigo-Mariani. Comparing high accurate regression models for short-term load forecasting in smart buildings. 46th Annual Conference of the IEEE Industrial Electronics Society IECON 2020, Oct 2020, Singapore, Singapore. ⟨hal-03016192⟩
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