Publications

Description

Authors: Adamantios Bampoulas, Fabiano Pallonetto, Eleni Mangina, Donal P. Finn

Abstract: A key issue in energy flexibility assessment is the lack of a scalable practicable approach to quantify and characterise the flexibility of individual residential buildings from an (opens in a new window)integrated energy system perspective without the need to use complex simulation models. In this study, this problem is addressed by explicitly quantifying the flexibility of multicomponent thermal and (opens in a new window)electrical systems commonly found in residential buildings based on an ensemble learning framework that consists of four algorithms, namely, random forests, multilayer (opens in a new window)perceptron (opens in a new window)neural network, (opens in a new window)support vector machine, and extreme gradient boosting. The day-ahead and hour-ahead prediction models developed are periodically updated considering dynamic feature selection based on residential occupancy patterns. The proposed methodology utilises synthetic data obtained from a calibrated white-box model of an all-electric residential building for two indicative occupancy profiles. The (opens in a new window)energy systems evaluated include a heat pump, a (opens in a new window)photovoltaic system, and a (opens in a new window)battery unit. The daily flexibility mappings are acquired by applying hourly independent, and consecutive demand response actions for each energy system considered, using suitable energy flexibility indicators. The results show that the ensemble models developed for each target variable outperform each of the constituent (opens in a new window)machine learning algorithms. Moreover, the storage capacity resulting from harnessing the heat pump downward flexibility demonstrates accurate accuracy with a coefficient of determination equal to 0.979 and 0.968 for day-ahead predictions and 0.998 and 0.978 for day ahead predictions for the two occupancy profiles considered, respectively. This framework can be used by electricity (opens in a new window)aggregators to evaluate a building portfolio in an end-user-tailored manner or optimally exploit its energy flexibility considering multi-step predictions to shift electricity usage to off-peak times or times of excess onsite (opens in a new window)renewable energy generation.