Simulation and Optimisation of Air Conditioning Systems using Machine Learning

TL;DR

This paper presents a deep learning approach using RNNs to predict future room temperatures and optimize air conditioning setpoints during unoccupied periods, significantly reducing energy consumption.

Contribution

It introduces a novel RNN-based model for temperature prediction and optimization of AC setpoints, outperforming existing models and demonstrating energy savings in real-world scenarios.

Findings

RNN models outperform state-of-the-art prediction models.

Optimized setpoints reduce energy use by approximately 20%.

Deep learning enables effective temperature forecasting for energy efficiency.

Abstract

In building management, usually static thermal setpoints are used to maintain the inside temperature of a building at a comfortable level irrespective of its occupancy. This strategy can cause a massive amount of energy wastage and therewith increase energy related expenses. This paper explores how to optimise the setpoints used in a particular room during its unoccupied periods using machine learning approaches. We introduce a deep-learning model based on Recurrent Neural Networks (RNN) that can predict the temperatures of a future period directly where a particular room is unoccupied and by using these predicted temperatures, we define the optimal thermal setpoints to be used inside the room during the unoccupied period. We show that RNNs are particularly suitable for this learning task as they enable us to learn across many relatively short series, which is necessary to focus on particular operation modes of the air conditioning (AC) system. We evaluate the prediction accuracy of our RNN model against a set of state-of-the-art models and are able to outperform those by a large margin. We furthermore analyse the usage of our RNN model in optimising the energy consumption of an AC system in a real-world scenario using the temperature data from a university lecture theatre. Based on the simulations, we show that our RNN model can lead to savings around 20% compared with the traditional temperature controlling model that does not use optimisation techniques.