Impact of HVAC Set Point Adjustment on Energy Savings and Peak Load Reductions in Buildings

Quantifying energy savings and peak demand reduction potentials in a building due to adjustment of HVAC set point during a demand response (DR) period is critical for active DR program participation and optimal demand-side resource allocations. This has been a topic of great challenge given the complex nature of physical characteristics and HVAC system thermal dynamics in commercial buildings. Based on the building model developed in eQUEST validated against measured power consumption data from a smart meter, this paper investigates how energy savings and peak demand reduction potentials of a building are impacted by HVAC set point changes during a DR period. Simulation results reveal that the days with high outdoor temperature are expected to achieve a predictable percentage of daily energy savings and peak load reductions when HVAC set points are raised in summer. However, the inevitable demand restrike, i.e., increase in building peak demand after a DR event, is a concern.

[1]  Sean P. Meyn,et al.  Experimental Evaluation of Frequency Regulation From Commercial Building HVAC Systems , 2015, IEEE Transactions on Smart Grid.

[2]  Bryan Eisenhower,et al.  Leveraging the analysis of parametric uncertainty for building energy model calibration , 2013 .

[3]  Xiao Chen,et al.  A data-driven state-space model of indoor thermal sensation using occupant feedback for low-energy buildings , 2015 .

[4]  David E. Claridge,et al.  Calibration Procedure for Energy Performance Simulation of a Commercial Building , 2003 .

[5]  Sylvain Robert,et al.  State of the art in building modelling and energy performances prediction: A review , 2013 .

[6]  Saifur Rahman,et al.  A self-learning algorithm for coordinated control of rooftop units in small- and medium-sized commercial buildings , 2017 .

[7]  Mohammed H. Albadi,et al.  A summary of demand response in electricity markets , 2008 .

[8]  Michael Stadler,et al.  Quantifying Flexibility of Commercial and Residential Loads for Demand Response using Setpoint Changes , 2016 .

[9]  Pedro J. Mago,et al.  Building hourly thermal load prediction using an indexed ARX model , 2012 .

[10]  Rune Hylsberg Jacobsen,et al.  Demand response potential of ventilation systems in residential buildings , 2016 .

[11]  Saifur Rahman,et al.  A peak-load reduction computing tool sensitive to commercial building environmental preferences , 2016 .

[12]  Johanna L. Mathieu,et al.  Quantifying Changes in Building Electricity Use, With Application to Demand Response , 2011, IEEE Transactions on Smart Grid.

[13]  Sishaj P. Simon,et al.  A spiking neural network (SNN) forecast engine for short-term electrical load forecasting , 2013, Appl. Soft Comput..

[14]  Frédéric Magoulès,et al.  A review on the prediction of building energy consumption , 2012 .

[15]  Peter Tzscheutschler,et al.  Short-term smart learning electrical load prediction algorithm for home energy management systems , 2015 .

[16]  Shengwei Wang,et al.  Development of prediction models for next-day building energy consumption and peak power demand using data mining techniques , 2014 .