• DocumentCode
    135712
  • Title

    Evaluation of energy saving potential using Stochastic Model Predictive Control for stand alone Air Conditioning units a study in Indian scenario

  • Author

    Ray, Tapabrata ; Majumdar, Shreyan ; Mukherjee, Sayan

  • Author_Institution
    Dept. of Archit., Jadavpur Univ., Kolkata, India
  • fYear
    2014
  • fDate
    16-17 Jan. 2014
  • Firstpage
    260
  • Lastpage
    265
  • Abstract
    Reduction in energy consumption has become imperative in the modern day. The building segment is responsible for almost 40% consumption. These installations are typically located at the distribution level. There are losses associated with the transmission system, such as T&D Losses and pilferage losses. Hence a single unit of energy saved at distribution level would amount to greater savings at the generation level. Developing nations rely largely on standalone Air Conditioning units for office and domestic use since these facilities rarely designed to accommodate centralized Heating ventilation and Air Conditioning (HVAC) systems. In this paper a novel approach to control all these air conditioning units using a centralized controller based on Stochastic Model Predictive Control (SMPC) has been presented. The SMPC takes into account the predicted weather to reduce energy consumption while maintaining the comfort level of the occupants. A sample office space has been modeled and performance of the algorithm has been studied for weather conditions of large cities of India. With centralized SMPC the system has significantly outperformed the existing SAC with localized controller.
  • Keywords
    HVAC; predictive control; stochastic systems; HVAC systems; Indian scenario; SMPC; T&D losses; building segment; centralized heating ventilation and air conditioning systems; comfort level; distribution level; energy consumption reduction; energy saving potential evaluation; generation level; localized controller; office space; pilferage losses; stand alone air conditioning units; stochastic model predictive control; transmission system; weather conditions; Analytical models; Capacitance; Educational institutions; Heating; Standards; Stochastic processes; Ventilation; Building Energy; Building Modeling; Energy Saving; Stochastic Model Predictive Control;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Non Conventional Energy (ICONCE), 2014 1st International Conference on
  • Conference_Location
    Kalyani
  • Print_ISBN
    978-1-4799-3339-6
  • Type

    conf

  • DOI
    10.1109/ICONCE.2014.6808742
  • Filename
    6808742