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Feuerriegel, S. and Neumann, D. (2016) Integration Scenarios of Demand Response into Electricity Markets: Load Shifting, Financial Savings and Policy Implications. Energy Policy, 96, 231-240. https://doi.org/10.1016/j.enpol.2016.05.050

has been cited by the following article:

• TITLE: Intelligent Building Load Scheduling Based on Multi-Objective Multi-Verse Algorithm

  AUTHORS: Jiangyong Liu, Jiankang Liu, Lv Fan, Lingzhi Yi, Huina Song, Qingna Zeng

  KEYWORDS: Intelligent Building, Load Scheduling, Multi-Objective Optimization, Multi-Objective Multi-Verse Algorithm

  JOURNAL NAME: Energy and Power Engineering, Vol.13 No.4B, April 8, 2021

  ABSTRACT: In the multi-objective of intelligent building load scheduling, aiming at the problem of how to select Pareto frontier scheme for multi-objective optimization algorithm, the current optimal scheme mechanism combined with multi-objective multi-verse algorithm is used to optimize the intelligent building load scheduling. The update mechanism is changed in updating the position of the universe, and the process of correction coding is omitted in the iterative process of the algorithm, which reduces the com-putational complexity. The feasibility and effectiveness of the proposed method are verified by the optimal scheduling experiments of residential loads.