• Energy Research

This paper proposes a prediction model based on fuzzy logic applied to anticipate electricity production and consumption in a building equipped with photovoltaics and connected to the grid. The goal is a smart energy management system able to make decisions and to adapt the consumption to the actual context and to the future electricity levels. The interest is to use as much electricity as possible from own production. The surplus is captured by an energy storage system or is sent to the grid. When no electricity is available from self-production, the grid is used to cover the necessities. The evaluations are performed on a data set collected in a real household. The proposed method is compared in terms of mean absolute error with other existing methods. The method developed based on fuzzy logic has an error of about 67 W, which places it among the most efficient models.

An effective energy management system relies on the accurate prediction of electricity consumption, facilitating energy suppliers to optimise energy distribution, reduce energy waste, and avoid overloading the power system. This paper analyses different methods for the estimation of electricity consumption at the level of an urban area. A statistical model based on Trigonometric seasonality, Box-Cox transformation, Auto-Regressive Moving Average errors, Trend and Seasonal components is first presented. Then a model based on fuzzy logic is also proposed. These methods will be optimised and evaluated on a dataset collected by the electric power supply agency of Sibiu, Romania, with the goal of reducing the forecast error. The models are also compared with a Markov stochastic model and with a Long Short-Term Memory neural model. The experiments have shown that our statistical model using a history length of 200 electricity consumption values and a daily seasonality is the most efficient, with the lowest mean absolute error of 3.6 MWh, thus making it a good candidate for integration into a city-level energy management system.