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Method for Cost-Benefit Analysis of Improved Indoor Climate Conditions and Reduced Energy Consumption in Office Buildings

Author

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  • Rokas Valancius

    (Faculty of Civil Engineering and Architecture, Kaunas University of Technology, Studentu g. 48, Kaunas LT-51367, Lithuania)

  • Andrius Jurelionis

    (Faculty of Civil Engineering and Architecture, Kaunas University of Technology, Studentu g. 48, Kaunas LT-51367, Lithuania)

  • Viktoras Dorosevas

    (Faculty of Civil Engineering and Architecture, Kaunas University of Technology, Studentu g. 48, Kaunas LT-51367, Lithuania)

Abstract

Indoor climate affects health and productivity of the occupants in office buildings, yet in many buildings of this type indoor climate conditions are not well-controlled due to insufficient heating or cooling capacity, high swings of external or internal heat loads, improper control or operation of heating, ventilation and air conditioning (HVAC) equipment, etc . However, maintenance of good indoor environmental conditions in buildings requires increased investments and possible higher energy consumption. This paper focuses on the relation between investment costs for retrofitting HVAC equipment as well as decreased energy use and improved performance of occupants in office buildings. The cost-benefit analysis implementation algorithm is presented in this paper, including energy survey of the building, estimation of occupants dissatisfied by key indoor climate indicators using questionnaire survey and measurements. Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) analysis is used in the proposed method for data processing. A case study of an office building is presented in order to introduce an application example of the proposed method. Results of the study verify the applicability of the proposed algorithm and TOPSIS analysis as a practical tool for office building surveys in order to maximize productivity by means of cost efficient technical building retrofitting solutions.

Suggested Citation

  • Rokas Valancius & Andrius Jurelionis & Viktoras Dorosevas, 2013. "Method for Cost-Benefit Analysis of Improved Indoor Climate Conditions and Reduced Energy Consumption in Office Buildings," Energies, MDPI, vol. 6(9), pages 1-16, September.
    • Handle: RePEc:gam:jeners:v:6:y:2013:i:9:p:4591-4606:d:28490
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    References listed on IDEAS

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    1. Tuominen, Pekka & Forsström, Juha & Honkatukia, Juha, 2013. "Economic effects of energy efficiency improvements in the Finnish building stock," Energy Policy, Elsevier, vol. 52(C), pages 181-189.
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    1. Stefano De Antonellis & Manuel Intini & Cesare Maria Joppolo & Calogero Leone, 2014. "Design Optimization of Heat Wheels for Energy Recovery in HVAC Systems," Energies, MDPI, vol. 7(11), pages 1-20, November.
    2. Fokaides, Paris A. & Jurelionis, Andrius & Gagyte, Laura & Kalogirou, Soteris A., 2016. "Mock target IR thermography for indoor air temperature measurement," Applied Energy, Elsevier, vol. 164(C), pages 676-685.
    3. Nuria Martín-Chivelet & Cecilia Guillén & Juan Francisco Trigo & José Herrero & Juan José Pérez & Faustino Chenlo, 2018. "Comparative Performance of Semi-Transparent PV Modules and Electrochromic Windows for Improving Energy Efficiency in Buildings," Energies, MDPI, vol. 11(6), pages 1-12, June.
    4. Cauchi, Nathalie & Macek, Karel & Abate, Alessandro, 2017. "Model-based predictive maintenance in building automation systems with user discomfort," Energy, Elsevier, vol. 138(C), pages 306-315.
    5. Lei Zhou & Yang Li & Beibei Wang & Zhe Wang & Xiaoqing Hu, 2015. "Provision of Supplementary Load Frequency Control via Aggregation of Air Conditioning Loads," Energies, MDPI, vol. 8(12), pages 1-20, December.
    6. Lei Chen & Yulong Pei & Feng Chai & Shukang Cheng, 2016. "Investigation of a Novel Mechanical to Thermal Energy Converter Based on the Inverse Problem of Electric Machines," Energies, MDPI, vol. 9(7), pages 1-19, July.
    7. Mario Collotta & Antonio Messineo & Giuseppina Nicolosi & Giovanni Pau, 2014. "A Dynamic Fuzzy Controller to Meet Thermal Comfort by Using Neural Network Forecasted Parameters as the Input," Energies, MDPI, vol. 7(8), pages 1-30, July.

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