Energy Audit Data Research Articles

Pathway to Decarbonization Through Industrial Energy Efficiency: Micro and Macro Perspectives from Compressed Air Usage

Energy audits directly provided the industrial sector with reduced energy costs and avoided emissions. Still, they also lead to far-reaching indirect and induced local, regional, and national benefits. This paper aims to present the techno-economic-environmental analysis to achieve decarbonization through implementing industrial energy efficiency at micro and macro levels. An integrated techno-economic-environmental methodology is developed. Case studies of micro-level carbon reduction efforts through industrial energy efficiency technologies are presented. The broader macroeconomic and environmental effects of technology on society are analyzed using data from 206 energy audits of industrial compressed air systems conducted over 13 years. The impacts show that energy-efficient improvements lead to direct cost savings for manufacturers, boost economic activity across sectors, and affect carbon dioxide emissions both short-term and long-term in the region. Given their extensive benefits, energy audits significantly influence policymaking. We devised a methodology to link micro-level energy audit data with macroeconomic and environmental analyses to quantify these cascading benefits. The economic scenario analysis shows that $228 M has been saved from direct industrial energy savings from implementing all compressed air recommendations in the studied periods and the region. In addition, the investment made through manufacturers would create 2,025 jobs and $383 M annually, cascading regional economic impacts. The environmental analysis shows that the regional manufacturers have directly avoided about 2.8 M metric tons of carbon dioxide emissions.

Energy Measurement and Potential Energy Conservation Measures in Five Healthcare Buildings in Malaysia

Concern is growing around the world as a result of the global economy's overall rise in carbon emissions and energy consumption. The healthcare facility is among the buildings in the world with the highest energy use. Thus, it is necessary to create energy-saving plans that evaluate the energy performance of buildings and put energy-saving measures into practice. Enhancing building energy efficiency is most commonly achieved through a comprehensive energy audit. The Building Energy Index is the current method used to analyses all forms of building energy performance. The operating hours of a healthcare facility are longer than those of a commercial building. The issue emerges when there is no clear criterion for establishing whether or not a healthcare facility is energy efficient, and the present norm may misread healthcare energy consumption. To determine the actual onsite energy use at the healthcare building, a full energy audit is performed. Five places are selected based on the sort of healthcare services offered. According to the on-site complete energy audit, cooling equipment consumes the most energy. The study's findings may be divided into two categories: Healthcare Building Energy Index from detailed energy audit results and derived directly from power bills for the years 2018 and 2019. In terms of Healthcare Building Energy Index, there is a contrast between utilizing detailed energy audit data and using utility bills directly. Nonetheless, a comparison of Healthcare Building Energy Index with existing Malaysian requirements reveals a significant discrepancy. Based on the audit and analysis results, several savings opportunities may be presented.

Prediction and scenario simulation of the carbon emissions of public buildings in the operation stage based on an energy audit in Xi'an, China

The carbon emissions (CEs) of public buildings (PBs) in the operation stage significantly impact building life cycle assessments (LCA). Energy conservation and carbon reduction during this stage is necessary for China to achieve peak CEs and neutralization. Energy consumption and CEs are analyzed using energy audit data for PBs in Xi'an. CEs of PBs in the operation stage are calculated by our prediction method with an average deviation of only 4.41%. Using the logarithmic mean Divisia index (LMDI) method to analyze factors influencing CEs, we find that economic development levels impact CEs the most in the operation stage; the impact coefficient reaches 3.81. This study discusses the contribution of various driving factors of CEs via the stochastic impacts by regression on population, affluence, and technology (STIRPAT) method and simulates seven scenarios to predict change trends of the CEs of PBs in the operation stage. The rough development scenario fails to achieve the peak, the comprehensive optimization development scenario achieves the peak in 2040, and the remaining scenarios achieve the peak in 2050. The results provide practical and theoretical support for CE research and a scientific basis for predicting the CEs of PBs in the operation stage throughout China and worldwide.

Analisis Intensitas Konsumsi Energi (IKE) pada Institut Teknologi Dirgantara Adisutjipto (ITDA) Yogyakarta

One of the main sectors that uses the largest electrical energy in a country is the commercial sector. One of the parts of this sector is education. This sector has a sub-sector. It is called a building. Electrical energy conservation efforts are needed to streamline electrical energy consumption by looking at energy-saving opportunities. This research was conducted at Halim Building and Wiweko Building in Institut Teknologi Dirgantara Adisutjipto (ITDA), Yogyakarta. The research was conducted by taking energy audit data regarding the level of electrical energy consumption as seen from the Energy Consumption Intensity (IKE) parameter. The research method began with the data collection. Furthermore, the data were processed using Ms. Excel, SPSS, and Matlab. Statistics analysis with ANOVA was carried out to conclude what parameters affected IKE. The results showed that the IKE value of the Halim Building was 30,48 kWh/m2/year and the IKE value of the Wiweko Building was 24,16 Wh/m2/year. The IKE value was still significant in the efficient category following ESDM & JICA Electric Power Development Co. LTD standards.

Private Hospital Energy Performance Benchmarking Using Energy Audit Data: An Italian Case Study

The increased focus on energy efficiency, both at the national and international levels, has fostered the diffusion and development of specific energy consumption benchmarks for most relevant economic sectors. In this context, energy-intensive facilities, such as hospitals and health structures, represent a unique case. Indeed, despite the high energy consumption of these structures, scientific literature lacks the presence of adequate energy performance benchmarks, especially in regard to the European context. Thus, this study aimed at defining energy benchmark indicators for the Italian private healthcare sector using data collected from the Italian mandatory energy audits according to Art.8 EU Directive 27/2012. The benchmark indicators’ definition was made using a methodology proposed by the Italian National Agency for New Technologies, Energy and Sustainable Economic Development (ENEA). This methodology provided the calculation of specific energy performance indicators (EnPIs) by considering the global energy consumption of the different sites and the sector’s relevant variables. The results obtained were compared with those obtained from a consolidated but more complex methodology: the one envisaged by the Environmental Protection Agency. The results obtained allowed us to validate the reliability of the proposed methodology, as well as the validity and future usability of the calculated indicators. Relying on a significant database containing actual data from recent energy audits, this study was thus able to provide an up-to-date and reliable benchmark for the private healthcare sector.

Analysis of Energy Use and Energy Savings: A Case Study of a Condiment Industry in India

Electric motors and boilers lead the industrial components which consume the largest portion of energy in an industry. This study explores the energy audit data of the condiment industry in India. The study mainly focuses on the estimation of the load factor, energy use, energy savings and annual bill savings with payback period of the electric motors of the plant. During the audit, it was found that there were several motors running under loaded conditions despite non-availability of variable speed drives installed in the plant. Therefore, variable speed drives are recommended to be installed to save energy by reducing the motors speed by up to 60%. According to the estimation, about 276 MWh, 551 MWh and 827 MWh electrical energy can be saved for 20%, 40% and 60% speed reduction of the motors using variable speed drives, respectively, where in most of the cases the payback period remains below 1 year. Furthermore, some suggestions are made to improve the poor power factor of running motors by using capacitor banks to save the reactive power. Besides, an estimation of energy saving is performed with a 2-ton capacity boiler. Since, there was no heat recovery system in the boiler; an air-preheater is suggested to be installed at the end of flue gas exhausting path of the boiler with the purpose of saving 68 tons of fuel per annum and having a payback period of 12 months. Moreover, a digital monitoring system, namely, “The Smart Joules” has been proposed to be installed in the plant aiming at saving about 3–5% of total energy per annum and having a payback period of 19 months. Finally, a summarization is made concluding in the fact that about 90 MWh energy and 95 tons of fuel can be saved (excluding motor energy savings) per annum by implementing proposed measures with a payback period of 15 months.

BuildingSync: A schema for commercial building energy audit data exchange

Numerous data formats exist to exchange building related information throughout a building's lifecycle; many are all-encompassing formats while some newer formats are focused on specific software data exchange between actors such as system to system, system to user, or user to user. Despite this, data format challenges occur during each iteration of a commercial building energy audit. To overcome these challenges, BuildingSync has been designed to harmonize building data exchange during building energy audits. The schema was developed to be interoperable to support various use cases such as reporting audits in an electronic format, tracking proposed, implemented, and discarded energy conservation measures, and storing building characteristics (at multiple levels) for audits, benchmarking, and building energy analysis. This article will review the commercial building data exchange landscape, detail the development of BuildingSync, and provide examples of its use in the built environment.

Will the Management Structure of Energy Administrators Affect the Achievement of the Electrical Efficiency Mandatory Target for Taiwan Factories?

Since 2014, Taiwan has promoted a 1% annual electricity saving target to promote electrical efficiency efforts. As the industrial sector accounts for approximately 60% of the overall electricity consumption of Taiwan, this sector presents the greatest opportunity for improving Taiwan’s overall energy efficiency. Here, the energy audit data of industrial energy users are analyzed via logistic regression to understand the factors impacting their likelihood of achieving the targeted 1% electricity saving. Of the variables under study, the number of employees and the rank of the energy administrator were significantly correlated with the likelihood of reaching the electricity saving target. Within the management structure of the factory, energy users with higher-ranking energy administrators are more likely to achieve the targeted 1% electricity saving. As it is impractical to rapidly increase the number of employees, higher-ranking employees, i.e., factory executives, should be appointed as energy administrators to improve users’ electrical efficiency and thus reach the targeted 1% annual electricity saving. Based on the findings of our research, we put forward a point of view that in addition to the introduction of new technologies to improve energy efficiency, it can also be achieved through adjustments to the management structure of energy administration.

Multi Kernel Learning based Sugar Industry Load Forecasting

Sugar industry which plans for power usage from Bagasse also needs the load forecasting carried out using the energy audit data. The stochastic nature of the load demand of the sugar industry needs to be forecasted in advance for the assuring uninterrupted power delivery to the industry. The manual energy audit data obtained from the sugar industry for a period of time is obtained and trained on a regression based on Multi Kernel Learning (MKL). The Support Vector Regression (SVR) formulation is applied with the Multi Kernel topology and the performance parameters including the Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) is observed in the implementation. The algorithm is the Multi Kernel Support Vector Regression algorithm using the Python based toolbox.

Dimension reduction of preference profile for aggregation of energy audit data

The paper aims to improve a technique based on preference aggregation, which allows to process a big amount of data from instrumental examinations of energy losses by substations of electrical energy distribution networks. An approach to transformation of the initial preference profile into the hierarchical one is considered. The absence of the influence of such a transformation on the correctness of processing results is shown. The workability of the proposed method is demonstrated on the basis of real energy surveys using the example of the Khakass enterprise of the Backbone Electric Grids (BEGs), being a part of Siberian BEG of the Unified National Electric Network of Russia. The method makes it possible to identify sources of economically inefficient expenditure of energy resources and unjustified energy losses, and also to provide compression of large volumes of energy survey data without diminution of essential information. The proposed method allows obtaining and representing a great amount of data of instrumental survey of substation energy losses in a form of a compact integral estimate in the ordinal scale. It can be an appropriate and promising tool for organizations engaged in energy consulting.

Dynamic Energy Performance Gap Analysis of a University Building: Case Studies at UAE University Campus, UAE

As a result of an increasing demand for energy-efficient buildings with a better experience of user comfort, the built environment sector needs to consider the prediction of building energy performance, which during the design phase, is achieved when a building is handed over and used. There is, however, significant evidence that shows that buildings do not perform as anticipated. This discrepancy is commonly described as the ‘energy performance gap’. Building energy audit and post occupancy evaluation (POE) are among the most efficient processes to identify and reduce the energy performance gap and improve indoor environmental quality by observing, monitoring, and the documentation of in-use buildings’ operating performance. In this study, a case study of UAE university buildings’ energy audit, POE, and dynamic simulation were carried out to first, identify factors of the dynamic energy performance gap, and then to identify the utility of the strategy for reducing the gap. Furthermore, the building energy audit data and POE were applied in order to validate and calibrate a dynamic simulation model. This research demonstrated that the case study building’s systems were not operating as designed and almost a quarter of the cooling energy was wasted due to the fault of the building facility management of the mechanical systems. The more research findings were discussed in the paper.

Energy optimization of a wastewater treatment plant based on energy audit data: small investment with high return.

Ambitious energy targets in the 2020 European climate and energy package have encouraged many stakeholders to explore and implement measures improving the energy efficiency of water and wastewater treatment facilities. Model-based process optimization can improve the energy efficiency of wastewater treatment plants (WWTP) with modest investment and a short payback period. However, such methods are not widely practiced due to the labor-intensive workload required for monitoring and data collection processes. This study offers a multi-step simulation-based methodology to evaluate and optimize the energy consumption of the largest Italian WWTP using limited, preliminary energy audit data. An integrated modeling platform linking wastewater treatment processes, energy demand, and production sub-models is developed. The model is calibrated using a stepwise procedure based on available data. Further, a scenario-based optimization approach is proposed to obtain the non-dominated and optimized performance of the WWTP. The results confirmed that up to 5000MWh annual energy saving in addition to improved effluent quality could be achieved in the studied case through operational changes only.

City-scale single family residential building energy consumption prediction using genetic algorithm-based Numerical Moment Matching technique

Growing energy consumption in urban areas has increased the importance of planning for future energy systems. Thus, improving the modeling abilities for predicting energy consumption at the city scale is critical. In this study, a Genetic Algorithm-Based Numerical Moment Matching (GA-NMM) method is adopted as a primary uncertainty estimation technique to predict the electricity consumption of a large dataset of single family homes by utilizing key features in energy audit and assessors data. This data is used as an input to the GA-NMM to develop a set of index buildings and associated weighting factors that represent statistical characteristics of the dataset. Energy models are then developed for the index buildings using physics-based energy modeling in EnergyPlus. These, in combination, are used to estimate the energy behavior of single family homes of the studied dataset. The proposed method is applied to a large dataset of 8370 single family homes in Cedar Falls, Iowa, where the expected annual and monthly electricity consumption from the model is calculated and compared with measured data. The expected site electricity consumption for single family buildings in Cedar Falls is estimated as 10,219 kWh/yr, which is within 6% of the measured average annual electricity consumption. At a monthly level, the Coefficient of Variation of Root Mean Square Error and Mean Bias Error are 7.8% and 4.5%, respectively. This method can be used to generate small set of representative homes for demonstrating the energy behavior of a larger set of homes.

Energy Cost Burdens for Low-Income and Minority Households in Six U.S. Cities: Evidence from Energy Benchmarking and Audit Data

Of the three primary components of housing affordability measures - housing cost, transportation costs, and utility costs - utility expenditures are the least understood, yet the one area where the cost burden can be reduced without household relocation. Existing data sources to measure energy costs are limited to surveys with small samples and low spatial and temporal resolution, such as the American Housing Survey and the Residential Energy Consumption Survey. This paper presents a new method to estimate household energy cost burdens that leverages actual building energy use data for 13,000 multi-family properties across six U.S. cities, and links energy costs to savings opportunities based on an analysis of 3,000 energy audit reports. We examine differentials in cost burdens across household demographic and socioeconomic characteristics, and analyze spatial, regional, and building-level variations in energy use and expenditures. We find that the average low-income household has an energy cost burden of 7%, whereas higher-income households have an average burden of 2%. Notably, we find that even within defined income bands, minority households experience higher energy cost burdens than non-Hispanic White households. For lower-income households, low-cost energy improvements could reduce energy costs by as much as $1,500 per year.

Applications of machine learning methods to identifying and predicting building retrofit opportunities

Building energy conservation measures (ECMs) can significantly lower greenhouse gas (GHG) emissions from urban areas; however, uncertainties regarding not only ECM eligibility, but also associated costs and energy savings have slowed adoption of ECMs. To encourage ECM implementation, local governments have implemented a range of policies designed to increase the available information on building energy use. Energy audit mandates, such as New York City (NYC)’s Local Law 87 (LL87), require energy consultants to analyze installed building systems and provide building stakeholders with cost effective ECM recommendations on a multi-year cycle. However, complete audits are costly and time consuming. To accelerate ECM implementation, policymakers are exploring ways to utilize available data to target ECMs across a city’s entire building stock. In this study, energy audit data for over 1100 buildings in NYC, submitted in compliance with LL87, are analyzed to identify opportunities for ECMs across building system categories (e.g. distribution system, domestic hot water, etc.). A machine learning classifier, specifically a user-facing falling rule list (FRL) classifier based on binary features derived from LL87 data, is developed here to predict ECM eligibility given a specific set of building characteristics. Overall, the trained FRL classifier performs well (ROC AUC 0.72–0.86) for predicting cooling system, distribution system, domestic hot water, fuel switching, lighting, and motors ECM opportunities, which represent a majority of the auditor-recommended ECMs in the sample. Additionally, linear decision lists developed by the model allow building stakeholders to easily conduct streamlined audits of building systems and identify possible ECM opportunities by limiting input to the most relevant factors and prioritizing likely retrofit candidates. The implications of this work are significant in accelerating the adoption of building ECMs and catalyzing energy use and GHG emissions reductions from buildings.

Information exchange requirements for building walk-through energy audits

Energy audits are generally considered the initial step of energy retrofits projects, paving the way for more detailed energy analysis and design. However, there are few standards to define the input and output data for energy auditing, making it difficult to leverage the data from an energy audit in the initial feasibility stage. Moreover, with the increasing use of building information modeling, the information exchange should be aligned with the future potential to support building information modeling implementation. This article describes the research on a standardized data structure for walk-through energy audits to help define the information exchange between energy audit data collection and energy analysis tools, namely EnergyPlus. The building information modeling-enabled energy modeling information exchange requirements were also compared with the current energy audit data structure to identify the gaps and overlaps. This research found that only 41% of collected data could be transferred for energy modeling, and only 26% of the input data that are required by EnergyPlus were captured by current energy audit practice. The alignment of data was even less when compared with the information exchange requirement of the building information modeling-enabled energy modeling. The reasons for the low alignment are discussed in this article and the future research directions are suggested.

Energy audit data for a resort island in the South China Sea.

The data consists of actual generation-side auditing including the distribution of loads, seasonal load profiles, and types of loads as well as an analysis of local development planning of a resort island in the South China Sea. The data has been used to propose an optimal combination of hybrid renewable energy systems that able to mitigate the diesel fuel dependency on the island. The resort island selected is Tioman, as it represents the typical energy requirements of many resort islands in the South China Sea. The data presented are related to the research article “Optimal Combination of Solar, Wind, Micro-Hydro and Diesel Systems based on Actual Seasonal Load Profiles for a Resort Island in the South China Sea” [1].

Developing energy consumption benchmarks for buildings: Bank branches in Brazil

The energy consumption of buildings is an area of increasing importance, and developing economies like Brazil must start to consider the energy performance of existing buildings. The publication of national energy benchmarks is a fundamental step for understanding energy consumption in commercial buildings and developing energy efficiency programmes. A voluntary data gathering initiative by the Brazilian Sustainable Construction Council (CBCS) is producing the data necessary to develop national benchmarks. A methodology for benchmark development is proposed, using both statistical data and energy audit data to benchmark end-use energy consumption, with the use of wet-bulb cooling degree hours for climate correction. Benchmarks and climate corrections are developed for the energy consumption of bank branches in Brazil. A simple linear regression analysis of data from 1890 bank branches in 57 different climates provides the energy consumption benchmark, while thermal simulation of building performance is used to validate the results and provide an end-use breakdown in the different climates studied. This work provides the foundation for further work to develop and publish national benchmarks in other typologies.

성능진단 데이터로 보정된 모델을 이용한 기존건축물의 에너지시뮬레이션 기법

Abstract This paper represents a method of existing building energy simulation using energy audit data. Energy audit mustbe carried out for reasonable analysis, because characteristics of existing buildings such as efficiency of fan, pump, flow rate, pressure, COP and operating schedule could be changed during the building operation. These building characteristicsshould be measured to estimate actual energy consumption of the existing building. In this study, we conducted energy audit and calculated energy savings for a 7-stories building as a case-study. The energy audit data were used to calibrate the building model of EnergyPlus simulation. Baseline model validated according to M&V guideline index. As a result,building characteristics are significant parameters making a big impact on energy savings in existing buildings. Key words Existing buildings(기존 건축물), Energy simulation(에너지시뮬레이션), Energy audit(성능진단), Baseline model calibration(기준 모델 보정), Measurement and verification(M&V

Adoption of energy-efficiency measures in SMEs—An empirical analysis based on energy audit data from Germany

This paper empirically investigates factors driving the adoption of energy-efficiency measures by small and medium-sized enterprises (SMEs). Our analyses are based on cross-sectional data from SMEs which participated in a German energy audit program between 2008 and 2010. In general, our findings appear robust to alternative model specifications and are consistent with the theoretical and still scarce empirical literature on barriers to energy-efficiency in SMEs. More specifically, high investment costs, which are captured by subjective and objective proxies, appear to impede the adoption of energy-efficiency measures, even if these measures are deemed profitable. Similarly, we find that lack of capital slows the adoption of energy-efficiency measures, primarily for larger investments. Hence, investment subsidies or soft loans (for larger investments) may help accelerating the diffusion of energy-efficiency measures in SMEs. Other barriers were not found to be statistically significant. Finally, our findings provide evidence that the quality of energy audits affects the adoption of energy-efficiency measures. Hence, effective regulation should involve quality standards for energy audits, templates for audit reports or mandatory monitoring of energy audits.