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Renewable energies are challenging to forecast due to their intermittence. However, it is crucial for the energy transition to predict accurately what is going to be produced at different temporal resolution (short, mid or long term) to integrate them in the network. In this work, we investigate the short term horizon. We work in the practical setting of the day-ahead forecast for wind farms. The aim of this work is twofold: to help the transmission system operator (TSO) in its task of balancing the network and the market participants of the day-ahead spot market. Both tasks require to know what is going to be produced for the next day. In this work, we will try new Artificial Intelligence (i.e. AI) models for wind energy forecasting. We explore state-of-the-art Machine Learning and Deep Learning models like Random Forest, Extra Trees, Recurrent Neural Network (i.e. RNN) and Transformers. We also investigate new RNN cells (e.g. BRC, nBRC and hybrid). We create original architectures of RNNs and Transformers. To compare the models and assess the results, we use two datasets: the ORES and the Gefcom2014 dataset. The first dataset is built from ORES recording productions of wind farms located in Belgium and weather data produced by the MAR (Modèle Atmosphérique Régional) developed at the University of Liège. The second dataset is often used in the scientific community. Then, we perform a deep analysis of the results given by the best models on both datasets. Additionally, we provide perspectives of improvement and we discuss other interesting techniques to investigate further.
Deep Learning --- Machine Learning --- Energy --- Artificial Intelligence --- Energy Forecasting --- TSO --- Day-ahead forecast --- Renewable Energy --- Ingénierie, informatique & technologie > Sciences informatiques
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Photovoltaics, among the different renewable energy sources (RES), has become more popular. In recent years, however, many research topics have arisen as a result of the problems that are constantly faced in smart-grid and microgrid operations, such as forecasting of the output of power plant production, storage sizing, modeling, and control optimization of photovoltaic systems. Computational intelligence algorithms (evolutionary optimization, neural networks, fuzzy logic, etc.) have become more and more popular as alternative approaches to conventional techniques for solving problems such as modeling, identification, optimization, availability prediction, forecasting, sizing, and control of stand-alone, grid-connected, and hybrid photovoltaic systems. This Special Issue will investigate the most recent developments and research on solar power systems. This Special Issue “Computational Intelligence in Photovoltaic Systems” is highly recommended for readers with an interest in the various aspects of solar power systems, and includes 10 original research papers covering relevant progress in the following (non-exhaustive) fields: Forecasting techniques (deterministic, stochastic, etc.); DC/AC converter control and maximum power point tracking techniques; Sizing and optimization of photovoltaic system components; Photovoltaics modeling and parameter estimation; Maintenance and reliability modeling; Decision processes for grid operators.
artificial neural network --- online diagnosis --- genetic algorithm --- renewable energy --- unit commitment --- photovoltaic panel --- power forecasting --- metaheuristic --- monitoring system --- embedded systems --- firefly algorithm --- tracking system --- MPPT algorithm --- integrated storage --- day-ahead forecast --- solar radiation --- prototype model --- artificial neural networks --- parameter extraction --- thermal image --- thermal model --- solar cell --- PV cell temperature --- evolutionary algorithms --- uncertainty --- battery --- harmony search meta-heuristic algorithm --- single-diode photovoltaic model --- symbiotic organisms search --- photovoltaics --- tilt angle --- smart photovoltaic system blind --- orientation --- photovoltaic --- particle swarm optimization --- analytical methods --- computational intelligence --- statistical errors --- ensemble methods --- solar photovoltaic --- electrical parameters --- demand response --- metaheuristic algorithm
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