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Energy cost and consumption reduction of an office building by Chaotic Satin Bowerbird Optimization Algorithm with model predictive control and artificial neural network: A case study

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  • Chen, Xiao
  • Cao, Benyi
  • Pouramini, Somayeh

Abstract

A large amount of energy consumed globally is done by buildings, also, buildings are responsible for a great portion of greenhouse gas emissions. With progress in smart sensors and devices, a new generation of smarter and more context-aware building controllers can be developed. Consequently, zone-level surrogate artificial neural networks are used herein, where indoor temperature, occupancy, and weather data are the inputs. A new metaheuristic optimization algorithm, called Chaotic Satin Bowerbird Optimization Algorithm (CSBOA) is employed for the minimization of energy consumption. 24-hour schedules of the heating setpoint of each zone are created for an office building located in Edinburgh, Scotland. Two modes of optimization including day-ahead and model predictive control are applied for each hour. The consumption of energy decreased by 26% during a test week in Feb in comparison to the base case approach of heating. By definition of a time-of-use tariff, the cost of energy consumption is decreased by around 28%.

Suggested Citation

  • Chen, Xiao & Cao, Benyi & Pouramini, Somayeh, 2023. "Energy cost and consumption reduction of an office building by Chaotic Satin Bowerbird Optimization Algorithm with model predictive control and artificial neural network: A case study," Energy, Elsevier, vol. 270(C).
    • Handle: RePEc:eee:energy:v:270:y:2023:i:c:s0360544223002682
    DOI: 10.1016/j.energy.2023.126874
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    Cited by:

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    2. Wang, Chuan'an & Pouramini, Somayeh, 2024. "Multi-objective modified satin Bowerbird optimization algorithm used for simulation-based energy consumption optimization of yearly energy demand of lighting and cooling in a test case room," Energy, Elsevier, vol. 292(C).
    3. Morovat, Navid & Athienitis, Andreas K. & Candanedo, José Agustín & Nouanegue, Hervé Frank, 2024. "Heuristic model predictive control implementation to activate energy flexibility in a fully electric school building," Energy, Elsevier, vol. 296(C).
    4. Yongjie Yang & Yulong Li & Yan Cai & Hui Tang & Peng Xu, 2024. "Data-Driven Golden Jackal Optimization–Long Short-Term Memory Short-Term Energy-Consumption Prediction and Optimization System," Energies, MDPI, vol. 17(15), pages 1-20, July.
    5. Tsu-Yang Wu & Haonan Li & Shu-Chuan Chu, 2023. "CPPE: An Improved Phasmatodea Population Evolution Algorithm with Chaotic Maps," Mathematics, MDPI, vol. 11(9), pages 1-21, April.
    6. Xiong, Suqin & Li, Yang & Li, Qiuyang & Ye, Zhishan & Pouramini, Somayeh, 2024. "Energy consumption prediction by modified fish migration optimization algorithm: City single-family homes," Applied Energy, Elsevier, vol. 353(PA).
    7. Lee, Chien-Chiang & Hussain, Jafar, 2023. "Energy sustainability under the COVID-19 outbreak: Electricity break-off policy to minimize electricity market crises," Energy Economics, Elsevier, vol. 125(C).
    8. Zhengran Cao & Chuandong Li & Man-Fai Leung, 2024. "The Synchronisation Problem of Chaotic Neural Networks Based on Saturation Impulsive Control and Intermittent Control," Mathematics, MDPI, vol. 12(1), pages 1-20, January.

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