Titlebook: Artificial Intelligence and Heuristics for Smart Energy Efficiency in Smart Cities; Case Study: Tipasa, Mustapha Hatti Conference proceedi

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https://doi.org/10.1007/978-3-031-62806-1 on the K-Nearest Neighbor (KNN) classifier. The system is implemented and simulated in Anaconda, and its performance is tested on a real dataset that contains 8 features and 768 instances to classify the cooling load magnitude into four (04) classes (4 target name labels) created based on the captu

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Hydrological Modeling of Urbanized Basinsve the optimal power flow management (OPFM) of the Algerian electric transmission system 114-Bus. Three objective functions have been optimized, the total fuel cost, the total power losses and total voltage deviation. The particularity and efficiency of the proposed MFO algorithm in terms of solutio

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https://doi.org/10.1007/978-3-031-59611-7CS), the main goal is to control the active and reactive stator powers of the Doubly Fed Induction Generator (DFIG) and the wind turbine speed generated by the Maximum Power Point Tracking (MPPT)..Particle Swarm Optimization (PSO) is used to tune the parameters of five sliding surfaces, by selecting

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Hydrological Modeling of Urbanized Basinss (HRES) problem, including photovoltaic panels (PV), wind turbines and battery storage. In this context, we have developed an automatic generation approach based on fuzzy rules through a Fuzzy-Adaptive-PSO approach. This approach allows to dynamically modify the rate of the acceleration coefficient