267 documents found, page 1 of 27
Anomaly detection in all-sky images: An approach using robust ensemble modeling...
Rocha, Vinicius Roggério da; Fisch, Gilberto; Costa, Rodrigo Santos; Ruano, AntonioAll-sky images (ASI) are widely used for sky monitoring, particularly in solar energy generation applications. Alignment issues and interferences demand a detection process of problematic images. With high sampling frequencies (1-2 images per minute), automating this process is crucial for managing large datasets and enabling integration into automatic systems, which is the objective of this work. For this purp...
A Multi-Step ensemble approach for energy community Day-Ahead Net Load point an...
Ruano, Maria; Ruano, AntonioThe incorporation of renewable energy systems in the world energy system has been steadily increasing during the last few years. In terms of the building sector, the usual consumers are becoming increasingly prosumers, and the trend is that communities of energy, whose households share produced electricity, will increase in number in the future. Another observed tendency is that the aggregator (the entity that ...
A new evolutionary forest model via incremental tree selection for short-term g...
El-Amarty, Naima; Marzouq, Manal; El Fadili, Hakim; Bennani, Saad Dosse; Ruano, Antonio; Rabehi, AbdelazizThe increasing integration of solar sources into the energy mix presents significant challenges, particularly in short-term energy management. Accurate solar irradiance forecasts can greatly assist solar power plant operators and energy network managers in making informed decisions about energy production and consumption. This paper aims to develop a new accurate forecasting model for short-term global solar ir...
Designing Robust Forecasting Ensembles of Data-Driven Models with a Multi-Objec...
Ruano, Antonio; Ruano, Maria da GraçaThis work proposes a procedure for the multi-objective design of a robust forecasting ensemble of data-driven models. Starting with a data-selection algorithm, a multi-objective genetic algorithm is then executed, performing topology and feature selection, as well as parameter estimation. From the set of non-dominated or preferential models, a smaller sub-set is chosen to form the ensemble. Prediction intervals...
Designing robust forecasting ensembles of Data-Driven Models with a Multi-Objec...
Ruano, Antonio; Ruano, MariaThis work proposes a procedure for the multi-objective design of a robust forecasting ensemble of data-driven models. Starting with a data-selection algorithm, a multi-objective genetic algorithm is then executed, performing topology and feature selection, as well as parameter estimation. From the set of non-dominated or preferential models, a smaller sub-set is chosen to form the ensemble. Prediction intervals...
MILP-based model predictive control for home energy management systems: A real ...
Gomes, I.L.R.; Ruano, Maria; Ruano, AntonioThis paper addresses the development of an innovative home energy management system (HEMS). The presented HEMS relies on a mixed-integer linear programming (MILP)-based model predictive control. The system takes advantage of the powerful formulation capabilities of a MILP-based mathematical pro-gramming problem with the capabilities of model predictive control to optimize, at each sample instant the HEMS operat...
From home energy management systems to communities energy managers: the use of ...
Gomes, Isaías; Graca Ruano, Maria; Ruano, AntonioThis 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 ...
Non-Intrusive Load Monitoring of Household Devices Using a Hybrid Deep Learning...
Laouali, Inoussa; Ruano, Antonio; Ruano, Maria da Graça; Bennani, Saad Dosse; Fadili, Hakim ElThe 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 fro...
Non-intrusive load monitoring of household devices using a hybrid deep learning...
Habou Laouali, Inoussa; Ruano, Antonio; Ruano, Maria da Graça; Bennani, Saad Dosse; Fadili, Hakim ElThe 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 fro...
A comprehensive review of solar irradiation estimation and forecasting using ar...
El-Amarty, Naima; Marzouq, Manal; El Fadili, Hakim; Bennani, Saad Dosse; Ruano, AntonioSolar irradiation data are imperatively required for any solar energy-based project. The non-accessibility and uncertainty of these data can greatly affect the implementation, management, and performance of photovoltaic or thermal systems. Developing solar irradiation estimation and forecasting approaches is an effective way to overcome these issues. Practically, prediction approaches can help anticipate events...