A study on forecasting electricity production and consumption in smart cities and factories. (December 2019)

• Record Type: Journal Article
• Authors: Gellert, Arpad; Florea, Adrian; Fiore, Ugo; Palmieri, Francesco; Zanetti, Paolo
• Is Part Of: International journal of information management. Volume 49(2019)
• Page Start: 546
• Page End: 556
• Publication Date: 2019-12
• Subjects: Electricity prediction -- Markov chains -- Photovoltaics -- Energy storage -- Energy management system; Social sciences -- Information services -- Periodicals; Social sciences -- Research -- Periodicals; Information science -- Periodicals; Management information systems -- Periodicals; Knowledge management -- Periodicals; Sciences sociales -- Documentation, Services de -- Périodiques; Sciences sociales -- Recherche -- Périodiques; Sciences de l'information -- Périodiques; Systèmes d'information de gestion -- Périodiques; Information science; Management information systems; Social sciences -- Information services; Social sciences -- Research; Periodicals; Electronic journals; 025.52068
• DOI: 10.1016/j.ijinfomgt.2019.01.006
• Languages: English
• ISSNs: 0268-4012

Abstract

Highlights: A method for forecasting energy demand and production is proposed. Predictions contribute to balance and smoothen the electricity intake from the power grid. Complexity is kept at a level compatible with implementation in hardware. Experimental evaluation is performed on data recorded on a real energy-management system. Abstract: The electrical power sector must undergo a thorough metamorphosis to achieve the ambitious targets in greenhouse gas reduction set forth in the Paris Agreement of 2015. Reducing uncertainty about demand and, in case of renewable electricity generation, supply is important for the determination of spot electricity prices. In this work we propose and evaluate a context-based technique to anticipate the electricity production and consumption in buildings. We focus on a household with photovoltaics and energy storage system. We analyze the efficiency of Markov chains, stride predictors and also their combination into a hybrid predictor in modelling the evolution of electricity production and consumption. All these methods anticipate electric power based on previous values. The main goal is to determine the best method and its optimal configuration which can be integrated into a (possibly hardware-based) intelligent energy management system. The role of such a system is to adjust and synchronize through prediction the electricity consumption and production in order to increase self-consumption, reducing thus the pressure over the power grid. The experiments performed on datasets collected from a real system show that the best evaluated predictor is the Markov chain configured with an electric power history of 100 values, a context of one electric power value and the interval size of 1.