Using Artificial Neural Network to Execute Demand Response Considering Indoor Thermal Comfort and Forecast Load-Shedding

Abstract:

This paper used artificial neural network to forecast the cooling load in the building in 24 hours. The unloading experiment kept the indoor thermal comfort at the ideal range of PMV=0~0.5 and PPD=5~10. Finally, dry bulb temperature, relative humidity, wet-bulb temperature and forecast cooling load were used for modeling by neural network. We can use this model to forecast how much load can be unloaded in summer peak hours accurately. This method controls the demand response for central air conditioning system, not only maintaining comfortable indoor environment, but also attaining the goals for reducing the electric expenses.

• Periodical: Applied Mechanics and Materials (Volumes 716-717)
• Pages: 1399-1408
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.716-717.1399
• Online since: December 2014
• Authors: Yung Chung Chang*, Jyun Ting Lu, Yu Chung Liu, Chun Hong Wang
• Keywords: Artificial Neural Network (ANN), Demand Response, Load Forecasting, Load-Shedding, Thermal Comfort

References

[1] Bureau of Energy, Ministry of Economic Affairs, Taiwan Green Productivity Foundation, Office Building Economization Manual, (2009).

[2] Chang, W.C., The Profit Maximization for the Bidding of Demand Response, master's thesis, Graduate Institute of Electrical Engineering, Tatung University, Taipei, (2012).

[3] Swider DJ, A comparison of empirically based steady-state models for vapor-compression liquid chillers, Applied Thermal Engineering 23 (5) April (2003) 539-556.

DOI: 10.1016/s1359-4311(02)00242-9

[4] Yung-Chung Chang, Sequencing of chillers by estimating chiller power consumption using artificial neural networks, Building and Environment 42 (2007) 180–188.

DOI: 10.1016/j.buildenv.2005.08.033

[5] Lin SF, Hung CA, Neural networks and pattern recognition, Taipei: Open Tech (2002).

[6] ISO 7730, Ergonomics of the thermal environment — Analytical determination and interpretation of thermal comfort using calculation of the PMV and PPD indices and local thermal comfort criteria, (2005).

DOI: 10.3403/30046382

[7] Taiwan Power Company, Preferential measures for user's power consumption reduction (revised, December 13, 2012) Retrieved from: http: /www. taipower. com. tw/UpFile/PowerSavFile/main_6_2_3. pdf.