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From home energy management systems to communities energy managers: the use of an intelligent aggregator in a community in Algarve, Portugal
Publication . Gomes, Isaías; Graca Ruano, Maria; Ruano, Antonio
This paper describes the development of community energy management systems (CEMS). A CEMS allows
optimal energy sharing within energy communities, as it is a central system that makes the global management of
the entire community. The proposed CEMS is based on mixed-integer linear programming (MILP), operating
under the receding horizon concept of Model Predictive Control (MPC). A systematic classification of electric
appliances, the use of external information such as weather information and energy prices, as well as the use of
intelligent forecasting techniques enables the proposed approach to achieve an excellent efficiency. It also allows
for an easy installation of as well as a smooth scaling with an increasing number of houses. The system is tested in
a real community in Algarve, Portugal. Different simulations are compared to experimental operation and
include cases with and without sharing of energy, different resources allocated to the houses considered, and the
use of different tariffs. CEMS formulations include sharing of energy without restriction, as well as employing
different allocation coefficients strategies. The results show that for the community under study when managed
by CEMS such as the one presented in this paper, it would result in significant cost reductions when compared to
the case where there is no energy community.
Home energy management systems with branch-and-bound model-based predictive control techniques
Publication . Bot, Karol; Habou Laouali, Inoussa; Ruano, Antonio; Ruano, Maria
At a global level, buildings constitute one of the most significant energy-consuming sectors.
Current energy policies in the EU and the U.S. emphasize that buildings, particularly those in the residential sector, should employ renewable energy and storage and efficiently control the total energy system. In this work, we propose a Home Energy Management System (HEMS) by employing a Model-Based Predictive Control (MBPC) framework, implemented using a Branch-and-Bound (BAB) algorithm. We discuss the selection of different parameters, such as time-step, to employ prediction and control horizons and the effect of the weather in the system performance. We compare the economic performance of the proposed approach against a real PV-battery system existing in a household equipped with several IoT devices, concluding that savings larger than 30% can be obtained, whether on sunny or cloudy days. To the best of our knowledge, these are excellent values compared with existing solutions available in the literature.
Non-intrusive load monitoring of household devices using a hybrid deep learning model through convex hull-based data selection
Publication . Habou Laouali, Inoussa; Ruano, Antonio; Ruano, Maria da Graça; Bennani, Saad Dosse; Fadili, Hakim El
The availability of smart meters and IoT technology has opened new opportunities, ranging from monitoring electrical energy to extracting various types of information related to household occupancy, and with the frequency of usage of different appliances. Non-intrusive load monitoring (NILM) allows users to disaggregate the usage of each device in the house using the total aggregated power signals collected from a smart meter that is typically installed in the household. It enables the monitoring of domestic appliance use without the need to install individual sensors for each device, thus minimizing electrical system complexities and associated costs. This paper proposes an NILM framework based on low frequency power data using a convex hull data selection approach and hybrid deep learning architecture. It employs a sliding window of aggregated active and reactive powers sampled at 1 Hz. A randomized approximation convex hull data selection approach performs the selection of the most informative vertices of the real convex hull. The hybrid deep learning architecture is composed of two models: a classification model based on a convolutional neural network trained with a regression model based on a bidirectional long-term memory neural network. The results obtained on the test dataset demonstrate the effectiveness of the proposed approach, achieving F1 values ranging from 0.95 to 0.99 for the four devices considered and estimation accuracy values between 0.88 and 0.98. These results compare favorably with the performance of existing approaches.
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Fundação para a Ciência e a Tecnologia
Funding programme
Concurso de Projetos de Investigação Científica e Desenvolvimento Tecnológico nos domínios Prioritários do Turismo, das Energias Renováveis e TIC - Programa Operacional do Algarve - 2018
Funding Award Number
SAICT-ALG/39578/2018