Best practice to build an accurate baseline model Samir Touzani stouzani@lbl.gov Lawrence Berkeley National Laboratory

Why accurate baseline models are important for M&V? A baseline model, which is usually developed using historical load data, predicts what would have been the load if the DR programs are usually evaluated using baseline If the baseline is over-estimated = under-estimation of load-shed performance: The DR programs are less attractive to customers, as they might think that their effort in load reductions are not fully acknowledged If the baseline is under-estimated = over-estimation of load-shed performance: The utilities/policy-makers are less motivated to promote the DR programs because the cost of the load-shed will be higher

How to build accurate baseline models? Data Preprocessing Data selection Acquisition of good weather data Data cleaning Baseline model estimation method Simple averaging models Regression models

Data Preprocessing Data Selection: Which period of the calendar year needs to be considered? Summer/Winter? Should the week-end/holidays be considered? What is the best granularity for load data? 60 min, 30 min or 15 min? 28 large commercial buildings and industrial facilities located in Central/Northern CA. ~12days/year of DR events between 2007 and 2009 Resolution Bias (kw) Std. dev. (kW) Max (kW) 30-min 0.1 2.22 14 60-min 0.88 4.59 22.7 Effect of data resolution Vs. 15-min on the shed estimates (Addy et al. 2015)

Data Preprocessing Acquisition of good weather data: Which source of data is more accurate? NCDC (National Oceanic and Atmospheric Administration) Weather underground Data source Bias (kw) Std. dev. (kW) Max (kW) Weather underground 0.39 23.36 148.3 Effect of outdoor air temperature data source on the shed estimates (Addy et al. 2015)

Data Preprocessing Data cleaning: Missing data Power outage Non routine events Non routine event example Filter Bias (kw) Std. dev. (kW) Max (kW) No-filter 4.25 30.08 394.2 Sensitive filter 2.51 18.55 151.4 Effect of power outage filter on the shed estimates (Addy et al. 2015)

Baseline models estimation methods Simple averaging models: Average of similar days model 10/10 model = average over the 10 previous working days 3/10 model = average over the highest energy-consuming 3 days of 10 working days preceding the DR event … Regression baseline models: Outdoor air temperature regression model (OATR) Li = ai + biTi Time of the week and temperature model (TOWT) Load is a function of time of the week Load is piecewise linear and continuous function of OAT Make a difference between occupied and non-occupied mode (automatically detected)

Baseline models estimation methods – Example 1 Pittsburg, Ca (school), October 13, 2010 (Max OAT: 93 F)

Baseline models estimation methods – Example 2

Baseline models estimation methods – Example 3 Average actual load, predicted load (TOWT model), and DR residual (Mathieu et al. 2011)

Conclusions A careful data preprocessing is an important step in order to produce an accurate baseline model All projects are not the same! For project with low load variability simple average models should produce accurate model For projects with high load variability, more complex baseline model such as TOWT may be required to achieve accurate estimates DR programs stakeholders should have more flexibility and options to select the most appropriate baseline modeling method

References Baseline model Ref.: Applications Ref.: Coughlin, K., Piette, M.A., Goldman, C. and Kiliccote, S., 2009. Statistical analysis of baseline load models for non-residential buildings. Energy and Buildings, 41(4), pp.374-381 Mathieu, J.L., Price, P.N., Kiliccote, S. and Piette, M.A., 2011. Quantifying changes in building electricity use, with application to demand response. IEEE Transactions on Smart Grid, 2(3), pp.507-518. https://bitbucket.org/berkeleylab/eetd-loadshape Addy, N.J., Kiliccote, S., Callaway, D.S. and Mathieu, J.L., 2015. How baseline model implementation choices affect demand response assessments. Journal of Solar Energy Engineering, 137(2), p.021008. Applications Ref.: Kiliccote, S., Piette, M.A., Dudley, J., 2010. Northwest open automated demand response technology demonstration project. Lawrence Berkeley National Laboratory. Page, J., 2012. Automated demand response technology demonstration project for small and medium commercial buildings.