A bottom-up and procedural calibration method for building energy simulation models based on hourly electricity submetering data. (15th December 2015)

• Record Type: Journal Article
• Authors: Ji, Ying; Xu, Peng
• Is Part Of: Energy. Volume 93:Part 2(2015)
• Page Start: 2337
• Page End: 2350
• Publication Date: 2015-12-15
• Subjects: Building energy simulation models -- Calibration -- Submetering data -- Cooling/heating load; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2015.10.109
• Languages: English
• ISSNs: 0360-5442

Abstract

Abstract: BESMs (building energy simulation models) play an important role in the design, optimization and retrofit of buildings. Developing a BESM is relatively simple in the building design phase because nearly all inputs are known from design parameters. However, in the building operation phase, developing and calibrating a BESM becomes difficult because all operating parameters must be adjusted according to real-time data. All of these parameters are difficult to measure, and they vary over time. Existing calibration methods of BESMs, which involve hundreds of input parameters, lack standard procedures and require specialized engineers. Engineers must randomly adjust input parameters until the output energy use matches measured energy use. To solve the problem above, a new calibration approach with a detailed procedure is proposed in this paper. This approach relies on electricity submetering data and HVAC (Heating, Ventilation and Air Conditioning) cooling/heating loads. These data are becoming more available in commercial buildings. A case study is demonstrated in a large commercial building with satisfying results. The CV (coefficient of variation) and MBE (mean bias error) of the total hourly electricity consumption simulation, excluding HVAC, are 4% and 3%, respectively. The CVs of an HVAC system are 12% (chiller), 6% (pump) and 5% (fan), and the MBEs are 10% (chiller), 5% (pump) and 4% (fan). Highlights: Model calibration method with submetering data and cooling/heating load is proposed. Method for calibrating lighting and equipment schedule is proposed. Method for calibrating the performance curve of HVAC equipment is proposed. A real commercial building is simulated and the result is satisfied.