Using Machine Learning to Predict Retrofit Effects for a Commercial Building Portfolio
Abstract
:1. Introduction
1.1. Motivation
1.2. Related Literature
1.2.1. The Applications of Data-Driven Methods in Existing Building Retrofit Studies
1.2.2. Advantage of Data-Driven Methods in Retrofit Savings Prediction
2. Database and Methodology
2.1. Data and Summary Statistics
2.2. Assumptions and Confounding Variables
2.3. The Causal Forest Model
3. Results
3.1. The Average and Distribution of Retrofit Effect
3.2. Model Performance
3.3. The Benefit of Targeting Buildings with Higher Savings
3.4. The Association between Predicted Savings and Weather
4. Discussion
4.1. Variable Importance of Input Features
4.2. Results Comparison with Similar Studies
5. Conclusions
- The sample size is still rather small, given the desire to evaluate six classes of energy conservation actions (ECM) and 21 sets of sub-actions.
- The ECM action classifications are broad with limited or conflicting indications of the sub-actions taken.
- The retrofit records only have a completion date, not a start date, such that before and after data sets may not be accurately defined. In addition, many actions share the same completion date, which could lead to the contamination of “pre-retrofit” data with “during-retrofit” data, potentially resulting in biased savings estimates.
- As previously discussed, Executive Orders 13,423 and 13,693 are strong policy drivers for buildings in the GSA portfolio to reduce their energy consumption, whether they are retrofitted or not. This might make the retrofit savings estimates more conservative and the same action might reach higher savings in buildings without such a policy driver.
- Due to some un-documented retrofit actions, some retrofitted buildings might be categorized as un-retrofitted in the study. This could also lead to a more conservative savings estimation. In the future, such uncertainty should be reduced by improving the retrofit action documentation or including a sensitivity analysis.
- Even though co-existing or past actions are controlled for with indicator variables, the interactions between actions might be more complicated and not adequately accounted for.
- Acquire larger data sets with more thorough documentation of retrofit details and building characteristics and covering a larger variety of retrofit actions. With this improvement, savings could be predicted more accurately for a larger set of more specific actions.
- The decision support section is relatively simple in this study, as the focus is more on the savings prediction, rather than decision optimization. More complicated multi-objective optimization methods in Section 1.2.1 could be incorporated.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Engineering Model or Projection | Empirical Approach | n | Realization Rate 1 | Building Use | References |
---|---|---|---|---|---|
NEAT | Experimental (RED) | around 30,000 | 30% in total energy | Residential | [8] |
TREAT | DD, event study | 101,881 | 35% in total energy 58% in energy expenses | Residential | [11] |
DEER | DD, IV | 275 | 79% in electricity for AC homes 0% in electricity for non-AC homes | Residential | [12] |
[13] | Experimental + engineering 2 | 504 | 87% in cooling 88–92% in heating | Residential | [14] |
World Bank | DD, event study, matching | 1,162,775 | About 25% in electricity reduction from fridge replacement | Residential | [15] |
Dwelling Energy Assessment Procedure (DEAP) | DD | 640,000 | 64 ± 8% | Residential | [16] |
Net Benefit Model | DD | 12,000 | 1/3 | Residential | [17] |
Not specified | Time-series approach | 2094 | 24% overall, 49% for HVAC, 42% for lighting | K-12 school | [18] |
Not specified | DD | 847 | 30–50% | Commercial and residential | [9] |
[19] | Repeated cross-sectional comparison controlling for observables | Around 7000 | <32% 3 | Residential | [20] |
Role of the Model | Model | Source |
---|---|---|
M&V inverse modeling | Linear regression | [35,36,37,38] |
Piecewise linear regression | [35,39,40] | |
Neural network (shallow) | [39,41,42] | |
Deep learning | [43] | |
Support vector machine regression (SVR) | [44,45] | |
Kernel regression | [42] | |
Gaussian process regression | [39,46] | |
Gaussian mixture regression | [39] | |
Decision tree | [47] | |
Random forest | [48] | |
Multi-objective optimization | Genetic algorithm (GA) | [49,50,51,52] |
Nondominated sorting genetic algorithm II (NSGA-II) | [53,54,55] | |
Particle swarm optimization | [52] | |
Sequential search | [52] | |
Predict retrofit decisions | Falling rule list | [30] |
Approximate BES results | Neural network (shallow) | [49,55,56] |
Generate inputs for BES parametric runs | Genetic algorithm (GA) | [57] |
Identify representative buildings | Clustering | [31,58] |
Estimate retrofit effect | Elastic nets | [34] |
Gradient forest | [34] | |
Causal forest | [33] |
Retrofit Effect Predictor | Min | Mean | Median | Max | St. Dev. | |
---|---|---|---|---|---|---|
withre trofit | Electricity (kBtu/sqft/year) | 1.85 | 41.67 | 38.72 | 287.25 | 20.89 |
Gas (kBtu/sqft/year) | 0.01 | 21.96 | 21.14 | 132.22 | 17.80 | |
Cooling degree day (CDD) | 58.6 | 1409.3 | 1160.2 | 4628.2 | 989.5 | |
Heating degree day (HDD) | 71.5 | 4484.1 | 4667.1 | 9172.7 | 2005.2 | |
Gross square footage | 5912 | 402,742 | 269,946 | 3993,881 | 456,913 | |
without retrofit | Electricity (kBtu/sqft/year) | 2.11 | 45.21 | 40.21 | 182.01 | 23.93 |
Gas (kBtu/sqft/year) | 0.00 | 29.94 | 21.07 | 283.68 | 30.77 | |
Cooling degree day (CDD) | 20.9 | 1398.5 | 1363.7 | 4589.0 | 961.2 | |
Heating degree day (HDD) | 184.3 | 4460.4 | 4188.3 | 10,580.2 | 2389.8 | |
Gross square footage | 1161 | 216,610 | 65,055 | 3456,919 | 428,105 |
Retrofit Effect Predictor Class | Retrofit Effect Predictor |
---|---|
Building characteristics | Building size in gross square footage. |
Whether a building is a historic building. | |
Whether a building had a LEED certificate before the retrofit project | |
Whether a building is an office building or a courthouse | |
Weather (average annual number of days with daily mean temperature within a certain range) | A total of 8 variables, the kth variable indicating the annual average number of days with daily temperature in the kth temperature bin. The temperature bins are below 10 °F, between 10 °F and 20 °F, …, between 80 and 90 °F, and above 90 °F. The 60 °F to 70 °F bin is left out as a reference. |
Climate | 1981–2010 climate normal of annual cooling degree day (CDD), and annual heating degree day (HDD) |
Pre-retrofit energy | Average monthly electricity, natural gas, chilled water, and steam consumption in kBtu/sqft/year. |
Region | A total of 10 indicator variables, the kth indicator variable representing whether a building is in GSA region k. Region 1 is left out as the reference level. |
Category | A total of 3 indicator variables, each corresponding to a GSA building category designation of B, E, or I. Category A is left out as the reference level 1. |
Previous actions | Indicator variables of pre- or co-existing action categories. |
Fuel | Action Type | Action | Summary Statistics | |||||
---|---|---|---|---|---|---|---|---|
Min | Q1 | Median | Q3 | Max | Mean | |||
Electricity | Capital | Building envelope | −5.09 | −2.84 | −0.82 | 1.58 | 4.47 | −0.66 |
HVAC | −5.02 | −1.86 | 1.53 | 4.76 | 11.71 | 1.90 | ||
Lighting | −4.36 | 0.52 | 2.20 | 4.53 | 14.20 | 2.55 | ||
Operational | Advanced metering | −1.41 | −0.09 | 0.36 | 0.93 | 2.86 | 0.47 | |
Commissioning | −7.15 | −2.75 | 1.56 | 3.57 | 11.00 | 0.94 | ||
GSALink | 1.04 | 3.40 | 4.26 | 5.57 | 10.60 | 4.87 | ||
Gas | Capital | Building envelope | −4.81 | −2.47 | −1.10 | 0.97 | 1.95 | −0.89 |
HVAC | −1.93 | 1.04 | 2.75 | 3.83 | 8.50 | 2.55 | ||
Lighting | −4.30 | −1.15 | 0.06 | 1.61 | 6.78 | 0.35 | ||
Operational | Advanced metering | −3.63 | −1.15 | −0.24 | 1.51 | 6.86 | 0.49 | |
Commissioning | −1.24 | 0.88 | 1.97 | 3.76 | 8.56 | 2.62 | ||
GSALink | 0.41 | 0.69 | 0.89 | 1.24 | 1.81 | 0.98 |
Retrofit Action | The Absolute Value of the Mean Forest Prediction Score—1 | The p-Value of the Differential Forest Prediction Score | ||
---|---|---|---|---|
Electricity | Gas | Electricity | Gas | |
Advanced metering | 1.78 | 0.87 | 0.94 | 0.04 * |
Building envelope | 0.37 | 0.13 | 0.07 | 0.49 |
Commissioning | 0.98 | 0.15 | 0.04 * | 0.34 |
GSALink | 0.19 | 1.55 | 0.95 | 0.93 |
HVAC | 0.45 | 0.01 | 0.00 *** | 0.27 |
Lighting | 0.13 | 1.41 | 0.21 | 0.73 |
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Xu, Y.; Loftness, V.; Severnini, E. Using Machine Learning to Predict Retrofit Effects for a Commercial Building Portfolio. Energies 2021, 14, 4334. https://doi.org/10.3390/en14144334
Xu Y, Loftness V, Severnini E. Using Machine Learning to Predict Retrofit Effects for a Commercial Building Portfolio. Energies. 2021; 14(14):4334. https://doi.org/10.3390/en14144334
Chicago/Turabian StyleXu, Yujie, Vivian Loftness, and Edson Severnini. 2021. "Using Machine Learning to Predict Retrofit Effects for a Commercial Building Portfolio" Energies 14, no. 14: 4334. https://doi.org/10.3390/en14144334
APA StyleXu, Y., Loftness, V., & Severnini, E. (2021). Using Machine Learning to Predict Retrofit Effects for a Commercial Building Portfolio. Energies, 14(14), 4334. https://doi.org/10.3390/en14144334