Engineering topics

Ravache, Baptiste

Publications and source records attributed to Ravache, Baptiste.

8 records

DOE OSTI2020

Accuracy of HVAC Load Predictions: Validation of EnergyPlus and DOE-2 using FLEXLAB Measurements

The aim of the project reported here was to better understand the level of accuracy of three building energy simulation (BES) engines (‘engines’) — EnergyPlus™, DOE-2.1e, and DOE-2.2 — by identifying and investigating significant deviations between the performance predicted by these engines and actual performance as measured in the FLEXLAB® test facility at Lawrence Berkeley National Laboratory (LBNL). The specific test conditions included some of those prescribed in ANSI/ASHRAE Standard 140 - Standard Method of Test for the Evaluation of Building Energy Analysis Computer Programs. Detailed measurements of FLEXLAB performance, including indoor temperatures and heat fluxes and air-flow and water flow rates and temperatures in the Heating, Ventilating and Air Conditioning (HVAC) system, together with hourly weather data, were recorded and used in analyzing the simulation results from EnergyPlus v8.8, DOE-2.2 v3.65 and DOE-2.1e v127. These engines are commonly used in the United States for building energy code compliance, federal, state, and utility incentives programs, as well as energy efficient design of new buildings and energy retrofit of existing buildings. Seven conventional overhead mixing ventilation scenarios were tested and each engine was found to have a similar level of agreement with the measurements of space-level heating and sensible cooling loads. These results provide useful information regarding the accuracy of these engines in predicting the cooling and heating load elements of whole building energy performance. This information is intended for practitioners who are concerned about transitioning between simulation tools with different engines and for managers of utility programs leveraging these tools for evaluating and/or projecting measure savings to be incentivized under their programs. The results of the comparisons of simulated and measured performance indicate that the predictions from all three engines are not significantly different. The 24-hour average value of the absolute mean bias indicates the likely magnitude of the error in any particular case. The average mean bias is reduced by cancelation of overprediction in one case by underprediction in another. The daytime absolute mean biases, which may be more important for both energy performance and occupant comfort, are ~6%, presumably because of the greater complexity involved in simulating in the presence of solar radiation. EnergyPlus typically overpredicts the cooling load and/or underpredicts the heating load by ~1.5% and the DOE-2 engines typically underpredict the cooling load by approximately the same amount. The Root Mean Square Error is relatively more sensitive to shorter term variations in the difference between predicted and measured loads; the three engines have similar values, ~10%, suggesting that the uncertainties in their predictions of peak loads may also be similar in magnitude. The implication of these results is that users, both designers and program analysts, can use EnergyPlus, DOE-2.1e, or DOE-2.2 to model conventional commercial buildings equipped with overhead mixing ventilation with a similar level of confidence. Further work is required to better understand the variability in the level of agreement between the engine predictions and FLEXLAB measurements, where a particular engine will agree well with FLEXLAB in some cases and not so well in others and another engine will agree or disagree in different cases. As the sources of this variability are identified and eliminated or reduced significantly, it is recommended that the experimental capabilities and methods developed in the study reported here should be applied to validating heating and cooling load calculations for spaces with different types of furniture and miscellaneous loads. These methods should then be applied to low energy space conditioning systems in EnergyPlus including, in particular, radiant slab and radiant ceiling panel cooling and heating systems and ‘mixed mode’ systems that combine mechanical cooling and natural ventilation systems, focusing on controls, including control of thermal mass. The work reported here addresses the conventional method of heating and cooling occupied spaces; other methods, such as the use of radiant heating and cooling systems have the potential to provide equivalent occupant comfort, or better, with lower energy consumption. These systems are addressed more explicitly in EnergyPlus but there is a need for empirical validation to give users the same level of confidence in modeling these systems that they have, or should have, in modeling conventional systems, based on the results presented here.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2019

Energy Modeling in Urban Districts: Forecast of Multi-Sector Energy Use and GHG Emissions

This document reports the findings of the energy use and greenhouse gases (GHG) emissions model loosely based on three districts in the San Francisco Bay Area, District A, District B, and District C. Modeling platforms exist for city energy benchmarking, inventorying, and GHG emissions forecasting and planning. However, the wide variety and features of today’s tools, their focus on a sub-set or snapshot data from various energy generation and consumption sectors, and the fact that many of them are not open data models, create sub-optimal environments for the energy analysis districts are seeking to conduct. Hence, we have developed a new software tool with customized data and dynamic visualization; DEPICT (Decision-support and Emissions Prediction Interactive Cities Tool) to obtain energy and emissions forecasts at different stages of the districts’ build-out by varying selected design parameters. This report presents the methodology and framework we have developed to estimate whole district emissions and details the results by district, with the objective of finding insights into the main sources of emissions, and the available levers to reduce them efficiently. This document details the impact of each emissions mitigation measure investigated, along with the assumption and models that were used to reach these values

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2019

Decision-support and Emissions Prediction Interactive Cities Tool

The Decision-support and Emissions Prediction Interactive Cities Tool (DEPICT) aims at predicting the cross-sectoral (building construction, operation and vehicles) energy consumption and greenhouse gases emissions at campus and urban scale. It leverages capabilities of multiple modeling tools: EnergyPlus for building operation, CalEEMod for building construction and gas vehicle, BEAM for electric vehicles and SAM for building–integrated photovoltaics. It distinguishes from other cross-sectoral model such as CalEEMod or BEST in that it uses physical models, rather than empirical models, for all sectors, which allows for a more accurate and customizable quantification. It is relevant for short-term multi-sectoral decision making by developers and city planners and long-term policy making by municipal and state officials. Information about all the developers

Ravache, Baptiste↗

DOE OSTI2019

Statistical change detection of building energy consumption: Applications to savings estimation

The surge in interval meter data availability and associated activity in energy data analytics has inspired new interest in advanced methods for building efficiency savings estimation. Statistical and machine learning approaches are being explored to improve the energy baseline models used to measure and verify savings. One outstanding challenge is the ability to identify and account for operational changes that may confound savings estimates. In the measurement and verification (M&V) context, ‘non-routine events’ (NREs) cause changes in building energy use that are not attributable to installed efficiency measures, and not accounted for in the baseline model’s independent variables. In the M&V process NREs must be accounted for as ‘adjustments’ to appropriately attribute the estimated energy savings to the specific efficiency interventions that were implemented. Currently this is a manual and custom process, conducted using professional judgment and engineering expertise. As such it remains a barrier in scaling and standardizing meter-based savings estimation. Here in this work, a data driven methodology was developed to (partially) automate, and therefore streamline the process of detecting NREs in the post-retrofit period and making associated savings adjustments. The proposed NRE detection algorithm is based on a statistical change point detection method and a dissimilarity metric. The dissimilarity metric measures the proximity between the actual time series of the post-retrofit energy consumption and the projected baseline, which is generated using a statistical baseline model. The suggested approach for NRE adjustment involves the NRE detection algorithm, the M&V practitioner, and a regression modeling algorithm. The performance of the detection and adjustment algorithm was evaluated using a simulation-generated test data set, and two benchmark algorithms. Results show a high true positive detection rate (75%-100% across the test cases), higher than ideal false positive detection rates (20%-70%), and low errors in energy adjustment (<0.7%). These results indicate that the algorithm holds for helping M&V practitioners to streamline the process of handling NREs. Moreover, the change point algorithm and underlying statistical principles could prove valuable for other building analytics applications such as anomaly detection and fault diagnostics.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2018

Stasis PCM Test Results

A phase change material product distributed by Stasis Group, designed to reduce the cooling energy requirements of multi-story commercial buildings, was tested in a side-by-side comparison at FLEXLAB® (LBNL, 2018), a calibrated test facility for low energy building technology solutions. The product was installed according to Stasis recommendations in the ceiling plenum of a 600 ft 2 cell representative of an office space, while an identical cell was operated in the same conditions, except for the night-time pre-cooling sequence and without the product, to gather baseline data. The cells were operated to replicate summer conditions of a candidate office building for retrofit. For the tests conducted at FLEXLAB, two Phase Change Material (PCM) compositions were tested, with the second product having an upgraded composition for thermal performance and upgraded packaging that was used to meet with fire safety requirements for a plenum application. Two rounds of tests were performed, which were designed to collect data for both core (designated as Round 1) and perimeter (Round 2) office space conditions. Following the analysis of the data collected during Round 1, the experimental design was modified to better represent the thermal conditions of a typical office space in relation to the PCM performance. A new product was also brought in for testing in Round 2, which had an improved operational temperature range and packaging properties. In Round 1, multiple tests were conducted to evaluate the performance of the product under different conditions of internal loads, temperature setpoints, pre-cooling strategies and temperature control strategies. In Round 2, the internal conditions were kept constant, while the test conditions only differ in the amount of PCM installed in the test cell plenum and the length of the nighttime pre-cooling sequence. The performance benefits of PCM are generally two-fold: the material may reduce peak cooling energy use during daytime hours (a peak demand energy use benefit) and may allow for energy savings with pre-cooling at night through night flush HVAC controls when night time outside air temperatures are lower, and fan energy use for cooling is more efficient than the use of daytime compressor-based cooling. The focus of this research project was mainly on quantifying the energy savings potential of the latter case although some effects on peak reduction were noted. Between the cell with PCM and nighttime precooling and the reference cell without PCM or precooling, a reduction in cooling load during occupied hours was noted between 7.6 kWh (or 22% of occupied hours cooling load) and 12.5 kWh (or 33% of occupied pg. 7 hours cooling load). Future work can further evaluate the peak cooling load reduction benefits of this product in more detail. This report highlights the results of Round 2, which reflect the performance of the modified PCM product when operated in suitable conditions. The results of Round 1, along with an analysis of the experimental data to identify the factors that prevented the product from performing as desired are presented in Appendix B.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2018

Do energy costs really affect commercial mortgage default risk? New results and implications for energy efficiency investments [PowerPoint]

This paper presents new results on the link between energy factors and commercial mortgage default. First, we summarize results from an empirical analysis of the impact of source energy use intensity (EUI) and electricity prices on mortgage default. We used a unique data set that merges building-level energy use data from city benchmarking ordinances and financial data for commercial mortgages on the same buildings. We found that building source EUI and electricity price are statistically and economically associated with commercial mortgage defaults. Next, we present five case studies on the impact of energy use and price variations on default risk: three office buildings, a hotel, and a multi-family residential building. We use the empirical model coefficients to compute the default risk impacts due to variations in source EUI and electricity price over the course of the mortgage term. We found that variations in source EUI could raise or lower the default rates in these properties by between 5% and 40%, depending on the property type and geography. Electricity pricing has an even greater effect – roughly 60% change in default rate in the Denver area and nearly 90% in northern California. Finally, we propose an energy risk score that lenders can use to assess energy risk and inform mortgage terms. The score is being developed and piloted in collaboration with three mortgage lenders. We conclude with implications of this score as a market signal and mechanism for incentivizing energy efficiency investments through the commercial mortgage channel.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2018

Transforming State-of-the-Art into Best Practice: A Guide for High-Performance Energy Efficient Buildings in India

India launched the Energy Conservation Building Code (ECBC) in 2007, and a revised version in 2017 as ambitious first steps towards promoting energy efficiency in the building sector. Pioneering early adopters—building owners, A&E firms, and energy consultants—have taken the lead to design customized solutions for their energy-efficient buildings. This Guide offers a synthesizing framework, critical lessons, and guidance to meet and exceed ECBC. Its whole-building lifecycle assurance framework provides a user-friendly methodology to achieve high performance in terms of energy, environmental, and societal impact. Class A offices are selected as a target typology, being a high-growth sector, with significant opportunities for energy savings. The practices may be extrapolated to other commercial building sectors, as well as extended to other regions with similar cultural, climatic, construction, and developmental contexts

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2017

Development of Automated Procedures to Generate Reference Building Models for ASHRAE Standard 90.1 and India’s Building Energy Code and Implementation in OpenStudio

This paper describes a software system for automatically generating a reference (baseline) building energy model from the proposed (as-designed) building energy model. This system is built using the OpenStudio Software Development Kit (SDK) and is designed to operate on building energy models in the OpenStudio file format.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗