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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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This book collects the publications of the special Topic Scientific advances in STEM: from Professor to students. The aim is to contribute to the advancement of the Science and Engineering fields and their impact on the industrial sector, which requires a multidisciplinary approach. University generates and transmits knowledge to serve society. Social demands continuously evolve, mainly because of cultural, scientific, and technological development. Researchers must contextualize the subjects they investigate to their application to the local industry and community organizations, frequently using a multidisciplinary point of view, to enhance the progress in a wide variety of fields (aeronautics, automotive, biomedical, electrical and renewable energy, communications, environmental, electronic components, etc.). Most investigations in the fields of science and engineering require the work of multidisciplinary teams, representing a stockpile of research projects in different stages (final year projects, master’s or doctoral studies). In this context, this Topic offers a framework for integrating interdisciplinary research, drawing together experimental and theoretical contributions in a wide variety of fields.

solar energy applications --- additive manufacturing --- coatings --- functional materials --- tribological and mechanical behavior --- bio residues --- biopolymer --- computer architecture --- artificial intelligence --- smart cities --- energy forecasting --- food --- sustainable cities and industries --- life cycle assessment --- emerging pollutants --- porous materials --- cellular and bacterial behavior --- powder technology

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Energy management systems (EMSs) are nowadays considered one of the most relevant technical solutions for enhancing the efficiency, reliability, and economy of smart micro/nanogrids, both in terrestrial and vehicular applications. For this reason, the recent technical literature includes numerous technical contributions on EMSs for residential/commercial/vehicular micro/nanogrids that encompass renewable generators and battery storage systems (BSS) The volume “Energy Management Systems for Optimal Operation of Electrical Micro/Nanogrids”, was released as a Special Issue of the journal Energies, published by MDPI, with the aim of expanding the knowledge on EMSs for the optimal operation of electrical micro/nanogrids by presenting topical and high-quality research papers that address open issues in the identified technical field. The volume is a collection of seven research papers authored by research teams from several countries, where different hot topics are accurately explored. The reader will have the possibility to benefit from original scientific results concerning, in particular, the following key topics: distribution systems; smart home/building; battery energy storage; demand uncertainty; energy forecasting; model predictive control; real-time control, microgrid planning; and electrical vehicles.

distribution systems --- smart home --- battery energy storage --- energy forecasting --- model predictive control --- real-time control --- microgrid --- black-start --- islanding --- master–slave control approach --- cold load pickup --- demand–response --- distributed electronic power converters --- optimal power sharing --- power flow control --- real-time simulations --- energy management system --- forecasting error --- rolling horizon --- demand uncertainty --- microgrids --- battery impedance model --- equivalent circuit model --- fractional-order model --- time- domain implementation --- efficient management --- energy resources --- heuristic approach --- nanogrid --- smart buildings --- micro-grid planning --- electrical vehicles --- energy storage --- flexible programming

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In the modern world, the competitiveness of bioenergy- and/or bioresources-related activities heavily depends on the effectiveness of supply chain management. A large number of multidisciplinary topics are involved in the bioresources and bioenergy production fields. Although the technical issues that are related with the topic are well-discussed and do not represent major barriers, supply chain management issues, such as design of the network, collection, storage or transportation of bioresources, are still considered as fundamental questions that need to be answered to enable the optimal exploitation of bioenergy and bioresources. Moreover, modeling of material and energy flows; identification of the dynamic character of the supply chains; available reverse logistics (waste management) alternatives; economic, social and environmental sustainability of bioresource supply chains; novelty in the applied business models; and decision support frameworks towards efficient supply chain management for bioenergy and bioresources present critical operational sustainability issues and business-making potential. This Special Issue, entitled “Supply Chain Management for Bioenergy and Bioresources”, seeks to contribute to the bioenergy and bioresources agenda through enhanced scientific and multi-disciplinary knowledge that may boost the performance efficiency of supply chain management and support the decision-making process of stakeholders. To that end, the Special Issue includes one extensive review on yellow and woody biomass supply-chain management, together with six original papers which span a number of innovative, multifaceted, technical developments that are related to all different echelons of supply chain management for bioenergy and bioresources.

Research & information: general --- Biology, life sciences --- Technology, engineering, agriculture --- supply-chain design --- strategic planning --- operational planning --- energy crop production --- crop residue --- dry above ground biomass --- soybean --- empirical models --- bilinear regression analysis --- agricultural operations --- energy use --- assessment tool --- workability --- machinery --- agricultural machinery --- fleet management --- auto-steering system --- collaborative operating system --- flow-shop --- simulation --- field experiment --- Fuzzy Cognitive Maps --- photovoltaic solar energy --- scenario analysis --- decision-support --- energy management --- bioenergy --- efficiency of bio-resources --- decision support system --- multi-criteria analysis --- sustainability --- neuro-fuzzy --- ANFIS --- neural networks --- soft computing --- fuzzy cognitive maps --- energy forecasting --- natural gas --- prediction

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In the modern world, the competitiveness of bioenergy- and/or bioresources-related activities heavily depends on the effectiveness of supply chain management. A large number of multidisciplinary topics are involved in the bioresources and bioenergy production fields. Although the technical issues that are related with the topic are well-discussed and do not represent major barriers, supply chain management issues, such as design of the network, collection, storage or transportation of bioresources, are still considered as fundamental questions that need to be answered to enable the optimal exploitation of bioenergy and bioresources. Moreover, modeling of material and energy flows; identification of the dynamic character of the supply chains; available reverse logistics (waste management) alternatives; economic, social and environmental sustainability of bioresource supply chains; novelty in the applied business models; and decision support frameworks towards efficient supply chain management for bioenergy and bioresources present critical operational sustainability issues and business-making potential. This Special Issue, entitled “Supply Chain Management for Bioenergy and Bioresources”, seeks to contribute to the bioenergy and bioresources agenda through enhanced scientific and multi-disciplinary knowledge that may boost the performance efficiency of supply chain management and support the decision-making process of stakeholders. To that end, the Special Issue includes one extensive review on yellow and woody biomass supply-chain management, together with six original papers which span a number of innovative, multifaceted, technical developments that are related to all different echelons of supply chain management for bioenergy and bioresources.

supply-chain design --- strategic planning --- operational planning --- energy crop production --- crop residue --- dry above ground biomass --- soybean --- empirical models --- bilinear regression analysis --- agricultural operations --- energy use --- assessment tool --- workability --- machinery --- agricultural machinery --- fleet management --- auto-steering system --- collaborative operating system --- flow-shop --- simulation --- field experiment --- Fuzzy Cognitive Maps --- photovoltaic solar energy --- scenario analysis --- decision-support --- energy management --- bioenergy --- efficiency of bio-resources --- decision support system --- multi-criteria analysis --- sustainability --- neuro-fuzzy --- ANFIS --- neural networks --- soft computing --- fuzzy cognitive maps --- energy forecasting --- natural gas --- prediction