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Day ahead electricity consumption forecasting with MOGUL learning model., , , , and . IJCNN, page 1-6. IEEE, (2018)Multi-Agent-Based CBR Recommender System for Intelligent Energy Management in Buildings., , , , , and . IEEE Syst. J., 13 (1): 1084-1095 (2019)Distributed Constrained Optimization Towards Effective Agent-Based Microgrid Energy Resource Management., , , , , and . EPIA (1), volume 11804 of Lecture Notes in Computer Science, page 438-449. Springer, (2019)Adaptive Learning in Multiagent Systems: A Forecasting Methodology Based on Error Analysis., , , , and . PAAMS (Special Sessions), volume 156 of Advances in Intelligent and Soft Computing, page 349-357. Springer, (2012)Adapting meeting tools to agent decision., , , , and . EAIS, page 32-39. IEEE, (2013)Economic Evaluation of Predictive Dispatch Model in MAS-Based Smart Home., , , , and . PAAMS (Special Sessions), volume 619 of Advances in Intelligent Systems and Computing, page 81-91. Springer, (2017)Electricity Markets Ontology to Support MASCEM's Simulations., , , , and . PAAMS (Workshops), volume 616 of Communications in Computer and Information Science, page 393-404. Springer, (2016)GA optimization technique for portfolio optimization of electricity market participation., , and . SSCI, page 1-7. IEEE, (2016)Ensemble learning for electricity consumption forecasting in office buildings., , , and . Neurocomputing, (2021)Adaptive Portfolio Optimization for Multiple Electricity Markets Participation., , , , , , , and . IEEE Trans. Neural Networks Learn. Syst., 27 (8): 1720-1733 (2016)