6533b85ffe1ef96bd12c2675
RESEARCH PRODUCT
A study on forecasting electricity production and consumption in smart cities and factories
Ugo FiorePaolo ZanettiFrancesco PalmieriAdrian FloreaArpad Gellertsubject
Energy storageComputer scienceComputer Networks and CommunicationsContext (language use)02 engineering and technologyLibrary and Information SciencesEnergy storageElectricity prediction; Energy management system; Energy storage; Markov chains; Photovoltaics; Information Systems; Computer Networks and Communications; Library and Information Sciences020204 information systems0502 economics and business0202 electrical engineering electronic engineering information engineeringProduction (economics)Energy management systemElectricity prediction; Energy management system; Energy storage; Markov chains; PhotovoltaicsMarkov chainsbusiness.industry05 social sciencesElectricity predictionEnvironmental economicsRenewable energyEnergy management systemPhotovoltaicsElectricity generation050211 marketingElectric powerElectricitybusinessInformation Systemsdescription
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.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2019-12-01 |