Surrogate model of a HVAC system for PV self-consumption maximisation

TL;DR

This paper introduces a surrogate modeling approach using active learning to efficiently predict building energy consumption, enabling better PV self-consumption and demand response strategies with significantly reduced computational effort.

Contribution

It presents a novel active learning-based surrogate model for building energy consumption, reducing simulation time and improving PV self-consumption optimization.

Findings

Surrogate model reduces simulation time by approximately 7 times.

Active learning effectively predicts voltage-current curves with fewer simulations.

Model aids in setting optimal reference temperatures for energy efficiency.

Abstract

In the last few years, energy efficiency has become a challenge. Not only mitigating environmental impact but reducing energy waste can lead to financial advantages. Buildings play an important role in this: they are among the biggest consumers. So, finding manners to reduce energy consumption is a way to minimise energy waste, and a technique for that is creating Demand Response (DR) strategies. This paper proposes a novel way to decrease computational effort of simulating the behaviour of a building using surrogate models based on active learning. Before going straight to the problem of a building, which is complex and computationally costly, the paper proposes the approach of active learning to a smaller problem: with reduced simulations, regress the curve of voltage versus current of a thermo-resistor. Then, the paper implements a surrogate model of energy consumption of a building. The goal is to be able to learn the consumption pattern based on a limited number of simulations. The result given by the surrogate can be used to set the reference temperature, maximising the PV self-consumption, and reducing energy usage from the grid. Thanks to the surrogate, the total time spent to map all possible consumption scenarios is reduced around 7 times.