Intelligent Consumer Flexibility Management With Neural Network-Based Planning and Control

Abstract

Optimal management of demand-side flexibility in buildings is important for properly integrating large amounts of intermittent generation from windmills and photovoltaics. This paper proposes a novel Energy Management Agent (EMA) concept that can optimize building’s energy costs with respect to external prices while at the same time allow building’s flexibility to be used via explicit demand response. The EMA combines Artificial Neural Networks (ANN) and model predictive control for modelling and optimization of building’s flexibility. It continuously manages building’s flexibility with respect to external prices and provides forecasts of the load and available flexibility for a defined time window. A proof-of-concept (PoC) of the EMA is implemented for controlling a heat pump in an apartment, located in Oulu, Finland. Two ANN-based models were implemented for modelling the energy consumption of the heat pump and the indoor temperature of the apartment. Monte Carlo Tree Search based planning and control was implemented for finding optimal control policies with the ANNs. The EMA PoC was evaluated in 16-week period between 11 November 2019 - 1 March, 2020. When compared to a fixed setpoint control strategy, the EMA achieved 14.8 % lower costs under Nord Pool spot prices for Finland. At the same time, it was also able to accurately follow the 24h load plans (NRMSE was 0.050) and activate the offered flexibilities (NRMSE was 0.074).

Keywords

• Demand response
• artificial neural network (ANN)
• optimal control
• model predictive control (MPC)
• optimization of HVAC system