Titlebook: Handbook of Smart Energy Systems; Michel Fathi,Enrico Zio,Panos M. Pardalos Living reference work 20210th edition Renewable energy.Artifi

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Application of Machine Learning in Occupant and Indoor Environment Behavior Modeling: Sensors, Methoccupant and indoor environment behavior modeling. In the first part of the chapter, various methodologies employed for non-intrusive occupancy status estimation, including the utilized sensors, feature generation methods, and detection algorithms, are reviewed. The second part is instead dedicated

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Economical and Reliable Design of a Hybrid Energy System in a Smart Grid Network,designed hybrid system’s efficiency. In our study, the associated costs in the objective function consist of initial investment costs, operational and maintenance costs, and the cost related to loss of load. To find the optimal solution with the nonlinear mixed-integer function, we utilized particle

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Energy Simulation Optimization for Building Insulation Materials,have become one of the most fundamental strategies preferred by governments. The heating and cooling demands have an important share in energy consumption in buildings. Therefore, thermal insulation systems have become the basic building elements to design energy-efficient buildings. Determining sui

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Machine Learning for Building Energy Modeling,d climate change-resilient smart energy systems in buildings. This chapter presents an overview of building energy modeling (BEM) using ML models and its implementation for the projection of building energy demand under future climate change scenarios generated by global circulation models (GCMs). I

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Big Data Applications for Improving the Reliability of the French Electricity Distribution Grid,illions of customers. Access to the electricity network is necessary for a major part of every day life, as well as for governmental services such as health and transport, especially in times of crisis. French territory is subject to the risk of storms, heat waves, thunderstorms, and floods. With th