Neural network controller for Active Demand-Side Management with PV energy in the residential sector

In this paper, we describe the development of a control system for Demand-Side Management in the residential sector with Distributed Generation. The electrical system under study incorporates local PV energy generation, an electricity storage system, connection to the grid and a home automation system. The distributed control system is composed of two modules: a scheduler and a coordinator, both implemented with neural networks. The control system enhances the local energy performance, scheduling the tasks demanded by the user and maximizing the use of local generation.

► We have developed a neural controller for Active Demand-Side Management.
► The controller consists of Multilayer Perceptrons evolved with a genetic algorithm.
► The architecture of the controller is distributed and modular.
► The simulations show that the electrical local behavior improves.
► Active Demand-Side Management helps users to control his energy behaviour.