Robust ensemble learning framework for day-ahead forecasting of household based energy consumption. (15th February 2018)

• Record Type: Journal Article
• Authors: Alobaidi, Mohammad H.; Chebana, Fateh; Meguid, Mohamed A.
• Is Part Of: Applied energy. Volume 212(2018)
• Page Start: 997
• Page End: 1012
• Publication Date: 2018-02-15
• Subjects: Household energy consumption -- Ensemble learning -- Robust regression -- Day-ahead energy forecasting; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2017.12.054
• Languages: English
• ISSNs: 0306-2619

Abstract

Highlights: An ensemble framework is proposed to forecast mean daily household energy usage. The methodology shows the use of the ensemble learner in smart energy systems. The utilized diversity parameters and robust integration produce a unique learner. The proposed ensemble framework is applied to a case study in France. Improved day-ahead energy usage forecasts are shown when compared to other models. Abstract: Smart energy management mandates a more decentralized energy infrastructure, entailing energy consumption information on a local level. Household-based energy consumption trends are becoming important to achieve reliable energy management for such local power systems. However, predicting energy consumption on a household level poses several challenges on technical and practical levels. The literature lacks studies addressing prediction of energy consumption on an individual household level. In order to provide a feasible solution, this paper presents a framework for predicting the average daily energy consumption of individual households. An ensemble method, utilizing information diversity, is proposed to predict the day-ahead average energy consumption. In order to further improve the generalization ability, a robust regression component is proposed in the ensemble integration. The use of such robust combiner has become possible due to the diversity parameters provided in the ensemble architecture. The proposed approach is applied to a case study in France. The results show significant improvement in the generalization ability as well as alleviation of several unstable-prediction problems, existing in other models. The results also provide insights on the ability of the suggested ensemble model to produce improved prediction performance with limited data, showing the validity of the ensemble learning identity in the proposed model. We demonstrate the conceptual benefit of ensemble learning, emphasizing on the requirement of diversity within datasets, given to sub-ensembles, rather than the common misconception of data availability requirement for improved prediction.