Multiobjective Optimal Scheduling Framework for HVAC Devices in Energy-Efficient Buildings

The worldwide energy consumption has been growing in aggregate at a tremendous rate, and a majority of the same is due to heating ventilation air conditioning (HVAC) loads in urban buildings. With the help of the recent advances in energy management and optimization techniques, the operations and fu...

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Bibliographic Details
Published in:IEEE systems journal 2019-12, Vol.13 (4), p.4398-4409
Main Authors: Chakraborty, Nilotpal, Mondal, Arijit, Mondal, Samrat
Format: Article
Language:English
Subjects:
Air conditioners
Air conditioning
Algorithms
Buildings
Circuits
Computer simulation
Energy consumption
Energy management
Greedy algorithm
Greedy algorithms
Heating systems
heating ventilation air conditioning (HVAC) scheduling
HVAC
HVAC equipment
Maintenance
Multiple objective analysis
Optimal scheduling
Optimization
Optimization techniques
Pareto optimization
Power demand
Power management
Production scheduling
Scheduling
smart building (SB)
Thermal comfort
Variation
Ventilation
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Summary:The worldwide energy consumption has been growing in aggregate at a tremendous rate, and a majority of the same is due to heating ventilation air conditioning (HVAC) loads in urban buildings. With the help of the recent advances in energy management and optimization techniques, the operations and functioning of these devices can now be managed and controlled efficiently for an improved energy consumption scenario and thereby reducing cost. In this article, we propose a multiobjective optimal scheduling framework based on Johnson's elementary circuit finding algorithm for controlling HVAC devices, specifically for buildings that require continuous thermal comfort maintenance. Two primary objectives addressed in this article are: minimizing power fluctuation and maximizing thermal comfortability of the users. We use standard comfortability indices to quantify thermal comfortability. To reduce the computation time, we also propose two intelligent improvement schemes that prune the exponential search space of Johnson's algorithm. Furthermore, a new greedy scheduling algorithm has been proposed to obtain near-optimal solutions efficiently. All the proposed approaches have been studied in a simulated environment depicting a real-world scenario to evaluate their efficiency and effectiveness for practical implementations, including a comparative analysis with Karp's minimum mean cycle algorithm in this problem setup.
ISSN:1932-8184
1937-9234
DOI:10.1109/JSYST.2019.2933308