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Site-Specific Hourly Methods Finalized

5/30/2018

Week Sixteen CalTRACK Update

During the standing meeting on 5/24, the working group finalized hourly methods for calculating hourly energy savings and commenced discussion on aggregating hourly savings into portfolio loadshapes. The finalized hourly methods and an introduction on aggregating portfolio loadshapes are outlined below.

View May 24, 2018 Working Group Meeting

Finalized Hourly Methods:

Data sufficiency for independent variables will be defined by coverage instead of the minimum time period required for daily and billing period methods.
The temperature variable will be defined by fixed temperature bins between 30-90 F instead of the variable degree day balance points used in billing period and daily methods.
In Lawrence Berkeley National Lab’s (LBNL) TOWT model, there is a model adaptation function that gives higher weight to recent data to improve short-term demand forecasts. This model adaptation function will not be used in CalTRACK’s hourly methods.
The occupancy variable will be defined with LBNL’s default occupancy algorithm. This is described in greater detail in Phil Price’s Everything I Know About Building Energy Modelling, But Never Told Anyone Before (18:30-30:00).

Aggregating Hourly Savings into Portfolio Loadshapes
To provide an accurate valuation of energy efficiency as a grid resource, energy savings must be quantified at specified time intervals and geographic locations. To create portfolio loadshapes, building-level savings must be aggregated. The method of aggregation has implications on the portfolio uncertainty and provides different granularity of information for aggregators, utilities, and customers. Different use cases may prefer different aggregation methods based on priorities specific to their use case. To accommodate different use cases, flexible methods for aggregating hourly savings into portfolio loadshapes may be preferred.
As we explore this topic further, some potential use cases to consider are:

Pay-for-Performance Programs
In the PG&E pay-for-performance program, the utility provides incentives for peak savings. This requires estimates of portfolio savings at the hourly level.

Non-Wires-Alternative Procurement
Non-Wireless-Alternative procurements require estimates of portfolio savings for buildings connected to specific grid nodes in order to measure grid impacts and potentially avoid infrastructure investments.

Cap and Trade, Greenhouse Gases, or Carbon Tracking or Trading Initiatives
Initiatives attempting to accurately quantify carbon offsets from energy efficiency investments require savings estimates at specified time and geographic locations because generation portfolios utilize resources with different carbon intensity at different times and locations.
We discussed a few options for aggregation methods, and look forward to input from the working group in the coming week.

Homework:

Review final Methods documentation and provide comments on GitHub (issue 101)

Review proposed Hourly guidelines on GitHub (issue 85)
Provide ideas on aggregation approaches for Portfolio Load shapes on GitHub (issue 97)
Contribute to the Sand Box of future issues.

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Time of Week and Temperature Model CalTRACK Application for Site Specific Hourly Savings

5/14/2018

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Week Fourteen & Fifteen CalTRACK Update

Review of hourly method proposals continued in week fourteen of CalTRACK 2.0 and will be finalized at the 5/24 working group meeting. Lawrence Berkeley National Lab’s Time-of-Week Temperature (TOWT) model is the specification to be used in CalTRACK 2.0. Mathieu et al. describe the application of TOWT models in Quantifying Changes in Building Electricity Use, with Application to Demand Response.

Overview of TOWT models:
As energy efficiency finds its legs as a grid resource, time dependent savings will be essential to the value proposition. Pay-for-performance programs can leverage this value with accurate building-level energy savings calculations at granular time intervals. TOWT models are one method for calculating energy savings at the hourly level.

Strengths:

Aggregated portfolios for pay-for-performance programs may have high variability in hourly energy consumption due to its nature. This variation is evident in figure 1, which shows load patterns over time for an office building, furniture store, and a bakery. The TOWT model addresses this variability in the following manner:
An “occupancy” proxy is determined using a linear regression model and allows for the hourly data to be segmented based on a building’s occupancy status.
Several “time-of-week” independent variables (one for each hour of the week) are included in the main linear regression model to capture hourly load variation. For example, a restaurant may regularly consume more energy on Friday nights because the restaurant has more customers on Friday nights. This type of variation will be controlled for by the “time-of-week” covariate.
The temperature covariate uses 7 bins of fixed temperature ranges instead of employing a grid search to find the balance points. Due to higher amounts of data in hourly methods, the fixed temperature ranges provide a simpler solution without significant drawbacks.

Weaknesses:

By nature, calculating hourly energy savings requires more granular data. This can make data sufficiency problematic.
Similar to daily methods, energy consumption on weekends or holidays may be different than typical days.
There is autocorrelation in the errors of parameter estimates, which complicates uncertainty calculations.

Homework:

Review draft of billing and daily methods write-up
Review proposals for hourly guidelines on GitHub
The next working group meeting is on Thursday, 5/24

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Considering Hourly Methods: Data & Use Cases

5/7/2018

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Week Thirteen Update for CalTRACK

During week thirteen, the CalTRACK working group discussed proposals for hourly methods in the standing meeting. The discussions included helpful suggestions of other reference materials as well as variations that may be appropriate for different applications of hourly methods and suggested improvements in CalTRACK 2.0’s documentation. The video from May 3, 2018 is provided at the end of this post.

Hourly Methods
In the development of hourly methods, the goal is to establish guidelines that mirror the methodology in billing period and daily methods. However, hourly methods have unique complexities that require departures from billing period and daily methods. These complexities are identified and discussed below:

Data Management
When compared to hourly data, daily and billing period savings calculations have higher data sufficiency requirements because hourly data contains more information per time period. This characteristic of hourly data supports the two adjustments to hourly data sufficiency requirements listed below:

 1. Usage data sufficiency will be specified in terms of data coverage (or common support) instead of a minimum time period:
Daily and billing period data sufficiency requirements impose a minimum quantity of time observed from a year of data. In hourly methods, usage data sufficiency will be specified in terms of data coverage in the independent variables. In Time of Week and Temperature (TOWT) models, the independent variables are temperature and occupancy. Data sufficiency requirements will be based on LBNL recommendations for data coverage.

2. Missing Data
Temperature has less variation between hours than days or billing periods. Smaller temperature variation between hours increases the likelihood that interpolated temperature values are accurate. For this reason, interpolated temperature values will be allowed in the reporting period for hourly methods. The threshold of allowable interpolated hours will be determined through empirical testing.

Recommendation for Pay-for-Performance Use Case:

In the baseline period, it is recommended to drop hours with missing temperature and usage data.
In the reporting period, a maximum of 6 missing values per day can be interpolated while maintaining minimum data sufficiency requirements.

Time Of Week Temperature (TOWT) Modeling Approach
The TOWT model, originally by Lawrence Berkeley Lab, contains two covariates:
Occupancy:
Occupancy is an indicator variable that takes the value of 1 if the building is occupied in the hour and 0 otherwise. In LBNL’s model, occupancy of a building is defined by:

Using ordinary least squares regression to establish a regression model for a building.
Grouping all observations at each hour. If 65% of the observations for an hour are above the established regression line, that hour is designated as occupied.
If the condition in (2) is not met, then the hour is defined as unoccupied

Temperature:
The TOWT model allows user-defined temperature bins for modeling a building’s weather dependence. We are recommending setting 7 fixed bins with endpoints at 30, 45, 55, 65, 75, 90, in order to cover a wide variety of climate conditions.

Use Case and Uncertainty

Time-aggregated Uncertainty:
In the program evaluation use case, an analyst may be interested in obtaining time-aggregated savings and uncertainty. Due to residual autocorrelation at the hourly level, aggregating hourly uncertainty for larger time intervals creates imprecise standard errors and uncertainty calculations. Instead, we recommend using daily methods with improved ASHRAE or Ordinary Least Squares (OLS) formulations of Fractional Savings Uncertainty (see Koran 2017) for aggregating uncertainty over time periods.

Hour-level Uncertainty Estimates:
For the procurement and pay-for-performance use cases, regression analysis is an effective tool for acquiring point estimates of savings and uncertainty at each hour. If each building is assumed to have independent errors, the uncertainty at each hour for all buildings in the portfolio can be aggregated without an autocorrelation problem.

Methods Documentation
Currently, the documentation for CalTRACK 2.0 is being updated. The first half of CalTRACK 2.0’s documentation will be posted on GitHub to allow the working group to review and comment on the changes in documentation.

Similar to the methods, the development of effective documentation is an iterative process. The documentation for CalTRACK 2.0 will improve by dividing into three distinct documents:

1. Methods
This document outlines the methodology for quantifying billing period, daily, and hourly energy savings while maintaining CalTRACK-compliancy. In CalTRACK 2.0, the Methods will be organized with a numbering system that corresponds to the Methodological Appendix. This will make referencing and accessing the appendix easier.

2. Methodological Appendix
The Methodological Appendix summarizes discussions and empirical testing that justify methodological decisions. The Methods will reference sections in the Methodological Appendix for readers to easily access empirical support for methodological decisions.

3. Field Guide
A document with minimum requirements for an implementation to maintain CalTRACK-compliancy. This is designed to be a practical and accessible checklist for analysts and other implementers of CalTRACK.

Ideas for Future CalTRACK Work
A sandbox has been added to the GitHub site to document proposals for participants to add ideas for future CalTRACK iterations. If you have an idea for CalTRACK 3.0, or beyond that cannot be addressed this year, please add it the the sandbox.

Additional Hourly Methods Resources

LBNL R Code on Time of Week and Temperature (TOWT)
2002 ASHRAE Guideline 14: Section 5 is relevant for data sufficiency requirements.
Uniform Methods Project – US Department of Energy
1. Peak Demand and Time-Differentiated Energy Savings Cross-Cutting Protocol
2. Whole-Building Retrofit with Consumption Data Analysis Evaluation Protocol

Homework

Review draft of billing and daily methods write-up
Review proposals for hourly guidelines on GitHub
The next working meeting is after 3 weeks on 5/23

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Site-Specific Hourly Methods Finalized

Time of Week and Temperature Model CalTRACK Application for Site Specific Hourly Savings

Considering Hourly Methods: Data & Use Cases

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