Baseline Energy Consumption Research Articles

Impact of voluntary green certification on building energy performance

Credible estimates of energy savings from green buildings are critical for policy makers to examine the cost and benefit of various incentives intended to encourage commercial buildings to go green (e.g. expedited construction permits, government grants, and property tax incentives). Yet, data limitations have hindered reliable estimations. Filling this gap, this study uses a large panel dataset on energy consumption for commercial buildings in Phoenix metropolitan area, Arizona. By tracking building occupants' monthly energy consumption before and after the building's certification as an Energy Star building, we provide new estimates of the environmental gains from private investment in green real estate. Results show that for occupants that occupy space in a certified building in both the pre-certification and post-certification periods, occupants in Energy Star buildings consume 8% less energy. This empirically robust estimate of potential benefit from green-certification provides a quantifiable benchmark against which green-promoting policies can be measured. We also document evidence of heterogeneous treatment effects. Energy savings differ by the building's initial certification points and the building's baseline energy consumption. These results are useful for policy makers to identify targets for green certification.

Specific energy consumption of cement in Thailand

According to the 2015 Thai Energy Conservation Plan, the government aims to reduce energy consumption by 37% in 2036 compared to the final energy consumption in 2010. The industry sector consumes about 35.7% of total energy consumption. Non-metallic sector accounts for 30.74% of the energy consumption of the industry sector. The cement industry is one of the non-metallic sector that consumes up to 60.69% of the energy consumption of the non-metallic sector. Specific energy consumption (SEC) and Baseline energy consumption (BEC) are used as a tool to assign energy conservation target for each industry sector. This paper presents BEC and SEC for cement production. There are 12 of 21 participating cement plants that provide relevant information to be used for BEC and SEC analysis. According to the analysis, the BEC and SEC of cement production does not depend only on the quantity produced, but also ambient temperature and humidity. Therefore, setting a typical BEC and/or SEC target for reducing energy consumption is not appropriate and unfair to the factory. The BEC and SEC equations are developed using statistical approaches. The cement productions are classified into 2 categories; clinker and cement. Regression equations are developed to determine the relationship between BEC/SEC and relevant variables, including BEC/SEC for electrical energy, thermal energy and total energy.

Benchmarking Energy consumption in Nigeria Universities

That energy is an important development factor is now common knowledge. Without energy, services and societal activities will virtually grind to a halt. In Nigeria, there are several federal and state universities that receive their funding from the government. Because of their peculiar nature as knowledge-transfer-based institutions, the energy source predominantly in use in the universities is electricity. Therefore, the issues of electric energy availability, consumption and costs in universities with resident students and staff quarters can present a formidable challenge to any responsible administration. Due to the inadequate and epileptic supply of electricity in our universities, this paper establishes the need for baseline energy consumption profile in the Universities as a framework for future energy savings scheme and policy formation. This will help to identify where energy is being wasted, together with appropriate energy efficiency measures that can be considered in order to reduce energy consumption, costs and mitigate associated environmental externalities.

A system-level approach for designing multi-family sustainable and energy-efficient housing communities

This study introduces a system-level methodology for designing multi-family sustainable housing communities. This methodology is flexible and user-friendly, and can be implemented by both researchers and design practitioners. It can be applied in assessment of various energy efficiency measures and energy delivery options that can be considered during the early stages of design and development. The overall goal of such a system-level process is to provide a quick and efficient approach for identifying various design trade-offs early in the design process, which leads to an acceptable final design.The authors have utilized a sustainable housing community that is under development in Bloomington, IN, U.S. as the case study to illustrate this methodology in the design process. Ten building variables that were deemed to have a significant influence on energy consumption and/or construction cost were considered for potential inclusion in the final design. During the process, a baseline building model was carefully defined according to applicable energy standards, available reference building models in the U.S., and community and developer preferences. All energy saving measures are considered relative to this baseline building model. The results of a sensitivity analysis show that advanced energy delivery options (such as geothermal heat pumps, energy recovery ventilators and heat pump water heaters) can significantly reduce the Energy Use Intensity (EUI) of the building as compared with adjustments to the envelope. In the final scenario, more than 45% of the annual baseline energy consumption is reduced from the initial design.

Specific energy consumption of sugar cane mills in thailand

According to Thai Energy Efficiency Plan (EEP 2015), the government has set the target of 30 % reduction in 2036 compared to the total energy consumption in 2010. The industry sector accounts for approximately 37 % of the total energy consumption. Thai industries have been classified into 11 main sectors, in which food, beverage and tobacco sector is the most energy-consumed sector which consume about 35.7 % of the total energy consumption. Among industries in the food, beverage and tobacco sector, sugar mill industry is the most second energy-consumed industry. Thus, the rational amount of energy saving target will be assigned to the sugar mill industry, for which their baseline energy consumption and their potential energy saving should be known. In this paper, 25 of 55 sugar mills are carefully investigated and audited to determine baseline energy consumption (BEC) and specific energy consumption (SEC) based on plant performance. Factors that effect on BEC and SEC are identified. Equations for predicting baseline BEC and SEC are developed using statistic regression approaches. It is found that the sugar mills can be classified into two groups, i.e. one with own power plant and one without power plant. They are totally different in BEC and SEC. The characteristic of energy consumption within a sugar mill itself is also different between juice extraction season and the others. The regression equations for predicted baseline BEC and SEC are developed based on known factors.

Assessing the energy saving potential of an existing high-rise office building stock

In underdeveloped countries, the lack of building energy consumption data makes it hard to decide upon interventions in the existing building stock. In order to overcome this difficulty this study develops a comprehensive framework to identify and analyse the energy saving potential of an existing building stock category. The methodology consists of six basic steps: a process for defining reference buildings, dynamic archetype model simulations, an energy consumption baseline estimation, energy retrofit measures selection and a cost optimal pathway analysis, development of energy saving scenarios and estimation of building stock energy savings potential. In order to apply the proposed methodology, a case study of the high-rise office building stock took place in the city of Belo Horizonte, Brazil. The energy consumption projection results for the next 20 years show that there are significant opportunities for energy efficiency improvements. Cost effective energy savings of up to 24% would be possible in the high-rise office building stock. Overall, energy saving scenario projections emphasize the urgency of dealing with the existing high-rise office building stock but also highlight that to reduce the energy consumption of the current building stock the policy makers should pay close attention to the new buildings.

Toward Building Energy Reduction Through Solar Energy Systems Retrofit Options: An Equest Model

Abstract Office buildings are responsible for a great portion of total energy consumption. In this study, solar system based retrofitting measures such as daylighting control system (DCS), Trombe wall (TW) and photovoltaic (PV) systems are modeled to an office building located in Dayton, Ohio, United States. An energy modeling tool, eQuest is utilized to analyze the economic and environmental impacts of the proposed single retrofitting measures along with the combined measure to identify the optimized building energy reduction opportunity. Compared to the baseline energy consumption, adopting single energy efficiency measures such as PV, DCS, TW, and overhangs/fins to windows results in about 25, 10, 9, 1 percentages of energy reduction respectively. In terms of economic perspectives, overhang and fins provide the best simply payback time around 1 year. Other solar system based retrofitting measures such as TW, DCS and PV can provide economic simple payback with 1.5, 2.5, and 12 years respectively. PV turned out to be the most costly options although it provides the largest energy savings which lead to the largest CO2 reductions. Adopting the combined system along with 50 kW photovoltaic array to the rooftop results in 45 percent office building energy reduction.

Modelling of an on-farm direct expansion bulk milk cooler to establish baseline energy consumption without milk pre-cooling: A case of Fort Hare Dairy Trust, South Africa

Storage of milk at a low temperature of 4°C inhibits bacterial growth. As such bulk milk coolers (BMC) are used for cooling and storage of milk on dairy farms. This paper presents performance monitoring of a direct expansion bulk milk cooler to establish its baseline energy consumption without milk pre-cooling through mathematical modelling. A data acquisition system comprising a power meter, a data logger, temperature sensors and relative humidity and ambient temperature sensors was constructed to capture the energy consumption of the BMC, room temperature and ambient conditions. On-farm milk records were used to determine milk production per milking session. Two milking times were considered, that is for the morning and the late afternoon periods. The average daily electrical energy consumption of the BMC for the two milking times was 48.31 kWh and 43.23 kWh, respectively. Mathematical models represented as multiple linear regression models were built and developed using the experimental data. The developed mathematical models gave good agreement with the experimental results as evidenced by correlation coefficients of 0.922 and 0.8995. ReliefF algorithm revealed that volume of milk is the principal contributor to the energy consumption of the BMC for both the AM and PM milking.

Gradient boosting machine for modeling the energy consumption of commercial buildings

Accurate savings estimations are important to promote energy efficiency projects and demonstrate their cost-effectiveness. The increasing presence of advanced metering infrastructure (AMI) in commercial buildings has resulted in a rising availability of high frequency interval data. These data can be used for a variety of energy efficiency applications such as demand response, fault detection and diagnosis, and heating, ventilation, and air conditioning (HVAC) optimization. This large amount of data has also opened the door to the use of advanced statistical learning models, which hold promise for providing accurate building baseline energy consumption predictions, and thus accurate saving estimations. The gradient boosting machine is a powerful machine learning algorithm that is gaining considerable traction in a wide range of data driven applications, such as ecology, computer vision, and biology. In the present work an energy consumption baseline modeling method based on a gradient boosting machine was proposed. To assess the performance of this method, a recently published testing procedure was used on a large dataset of 410 commercial buildings. The model training periods were varied and several prediction accuracy metrics were used to evaluate the model’s performance. The results show that using the gradient boosting machine model improved the R‐squared prediction accuracy and the CV(RMSE) in more than 80 percent of the cases, when compared to an industry best practice model that is based on piecewise linear regression, and to a random forest algorithm.

Explaining inefficiencies in commercial buildings providing power system ancillary services

Ancillary services are required to balance supply and demand in electric power systems. Demand response may provide attractive options for these services, through means such as varying the power consumption of commercial building HVAC systems. However, experimental results from a 30,000m2 office building suggest that when a building provides ancillary services it consumes more energy than when it is operated normally. This translates to additional costs and environmental impacts.This paper investigates potential causes for building inefficiency associated with ancillary services provision. We develop a physics-based simulation model that captures heat exchange processes and fan and air duct dynamics. During an ancillary service event, we vary the fan power consumption, and then compute the difference between the baseline and actual energy consumption to determine the efficiency of the actions. We explore the impact of building parameters, control design, and baseline model accuracy on the efficiency.In simulation, we find that shorter duration power changes and less aggressive controllers result in less change in energy consumption. We also find that baseline error has outsized effects on the efficiency calculations. These results offer new understanding of the mechanisms underlying inefficiency and point to opportunities to reduce associated costs and environmental burdens.

Energy mapping of existing building stock in Sweden – Analysis of data from Energy Performance Certificates

This study contributes to defining the current energy consumption baseline for buildings in Sweden. The data used for the analysis are extracted from the database of the Swedish National Board of Housing, Building and Planning and consists of 186,021 measured energy performance certificates issued for commercial buildings (355Mm2), collected during 2007–2015.The results from this study for certain building types, construction periods, climate zones and energy use is presented. Building codes have influence of the buildings energy performance. When new building codes have been adopted and energy performance requirement been stricter the measured energy consumption have been lowered compared to buildings built earlier. New buildings have nevertheless often higher energy consumption than stipulated by the building code.Climate zone have less impact on energy consumption than type of building. Building units in the warmer climate zones generally have slightly lower energy consumption than those in colder zones. This implies that the buildings are well adapted to the climates at hand. The results may help public authorities to plan future business strategies and energy policies.The Swedish Energy Performance Certificates are quite reliable because they are based on energy bills and not on theoretical calculations.

One size does not fit all: Establishing the need for targeted eco-feedback

Despite all improvements in buildings shell, equipment, and design, CO2 emissions from buildings are increasing. Since occupants spend more than 87% of their time indoors, they are inseparable and significant elements of building system dynamics. Hence, there is a great potential for energy efficiency in buildings using a wide range of programs such as intervention and eco-feedback. Despite the high level of individual differences and intra-class variability of occupants’ behaviors, the current state-of-the-art eco-feedback programs treat all the occupants uniformly and do not target and tailor the feedback. Therefore, it leaves an opportunity to increase the efficacy of eco-feedback systems through the designing of tailored and targeted programs. In this paper, we conducted a comprehensive analysis and tested hypotheses on occupants’ behavioral responses to a normative comparison feedback program, in addition to the impact of notifications on the level of engagement of each group of occupants. We categorized occupants who participated in the normative comparison feedback program into three groups (i.e. low, medium, and high energy consumers) based on their baseline energy consumption, and tested 9 hypotheses. A mixed-effect regression model (MRM) and a paired t-test was implemented to evaluate the proposed hypotheses. The hypotheses examine the variability of occupants’ responses under the same eco-feedback program, and the effectiveness of notifications on reinforcing occupants’ engagement in these programs. The contribution of this paper is two-fold: (1) reporting that the effectiveness of the notifications in eco-feedback programs are initially highly dependent on the type and the nature of the program, and then the interval and the content of the notification, and (2) demonstrating the variability of occupants’ behavioral responses under the same normative comparison eco-feedback program. These findings indicate the need for a shift in focus toward targeted and tailored feedback programs which treat occupants based on their characteristics. Moreover, they highlight the need for eco-feedback design, development, testing and implementation research that acknowledges and addresses differences in occupant responses to feedback.

Evaluation of perceived indoor environmental quality of five-star hotels in China: An application of online review analysis

Hotel guest perception of Indoor Environmental Quality (IEQ) is significant for both hotel management and IEQ research. This paper used a novel text-mining methodology to analyse guest perception of IEQ through online hotel reviews. More than 160,000 reviews from 70 five-star hotels were collected from Ctrip.com, the largest travel website in mainland China. Next, a list of IEQ keywords were programmed to extract specific IEQ problems from these reviews. Finally, a complaint indicator was proposed to evaluate customer IEQ dissatisfaction. The results showed an average of four complaints (about indoor environment) per one hundred online reviews. Air conditioning, noise, and humidity were the top three factors influencing guest IEQ satisfaction, representing 39, 23, and 19%, respectively. This paper also revealed that a higher IEQ complaint rate resulted in a lower online rating; as such, IEQ rate indirectly affected the business performance of the hotel. This research also considered the baseline energy consumption of 5-star hotels; if the annual energy consumption was lower than 150 kWh/m2a, then indoor environmental performance was significantly compromised. However, if energy consumption was higher than 150 kWh/m2a, then there was no obvious negative correlation between the IEQ complaint rate and energy consumption.

Sustainable Manufacturing Performance Indicators for a Serial Production Line

Current manufacturing facilities lack proper performance indicators that can accurately pinpoint areas of energy inefficiency on the manufacturing line. This paper establishes new indices that utilize readily available, real sensor information from the production line, such as buffer levels, machine throughput, etc., to find the machine that is the least energy efficient. This is the machine that causes the line to waste the most energy without producing parts. The energy structure of the production line is analyzed to better understand the complex system dynamics and to find the root cause of the energy inefficiencies. A baseline energy consumption is established, which is the least amount of energy that is needed to produce a certain number of parts on the line. Using this knowledge, the new sustainable manufacturing performance indicators are defined to properly monitor the performance of the line. These indices utilize the energy structure to illustrate the static (energy used when there are no downtime events) and dynamic (energy lost due to downtime) portions of the energy consumption. The concepts of the downtime energy bottleneck and the rated power bottleneck are introduced and proven analytically. These methods are verified using simulation studies in Simulink/MATLAB.

Energy mapping of existing building stock in Spain

Energy performance certificate databases are a key tool for mapping national building stock and thus fostering greater overall energy efficiency. This paper presents an insight into the energy performance of residential and tertiary sector buildings in Spain, through an analysis of the first 129,635 energy performance certificates issued for existing buildings, collected by the Catalan Institute of Energy. Most of the residential buildings or building units that were studied were “E” class (53.6%). Single-family houses were found to use more energy on average (248.0 kWhp/m2) than individual dwellings (183.2 kWhp/m2). Tertiary sector buildings were found to have slightly better energy performance (26.4% of buildings were rated “D class”), with an average energy consumption of 317.8 kWhp/m2. Modern buildings consume less energy, as they must meet the higher energy performance requirements stated in thermal building regulations. Residential buildings or building units located in hotter climate zones consume slightly less energy than those located in colder zones, mainly because heating accounts for a high percentage of overall energy expenditure (70–75% in residential buildings). A significant proportion of the energy consumed in tertiary sector buildings is for lighting (37.2%). This research defines the current energy consumption baseline of existing buildings in Spain. The results can help to prioritize energy conservation efforts according to building type, construction period, climate zone and specific end-uses. They may also help public authorities to plan future energy policies, and construction practitioners to identify market segments and business strategies.

A GIS domestic building framework to estimate energy end-use demand in UK sub-city areas

Abstract This paper presents the development, evaluation and application of a spatially referenced domestic building level framework (i.e. address level) to estimate domestic energy end-use demand baseline in sub-city areas. The paper core idea and conclusion is that unless knowledge and model estimating is available at an appropriate level, future UK local energy infrastructure planning will not be effective. Our framework innovatively combines a dataset, which includes detailed building surveys of 60,977 out of a total of 139,257 dwellings, with a normalised national dataset (i.e. the English Housing Survey) and applied to a BRE Domestic Energy Model (i.e. Cambridge Housing Model) so as to establish an energy consumption baseline for the domestic stock in localised areas of Newcastle upon Tyne. Our validation results show a poor alignment with existing observed data as published by the Department of Energy and Climate Change (DECC), particularly at neighbourhood scale. Our belief is that as spatial resolution is increased, local building and urban socio-economic and physical characteristics play a more important part in the estimation of dwelling energy consumption. Thus, we propose a taxonomy to holistically deal with the sources of uncertainty arising from these issues and the components of our framework.

Methodology to Increase Energy Efficiency in Discrete Manufacturing

The utilisation phase of machinery in discrete manufacturing operations is characterized by changing economical and technical requirements like capacity, performance and as emerging requirement reduced energy consumption. Established industry practices as well as upcoming standards mainly focus on improving the energy efficiency by developing new machinery. Especially existing factories and the machinery in use offers energy saving potentials to be identified and to be capitalized by implementing energy saving retrofit measures. By doing so, the use of existing manufacturing machinery leads to a sustainable use of manufacturing equipment. The discussed research work therefore includes an approach to interpret in-process measurement data and to derive electric energy savings potentials. Based on this assessment, improvement measures like dimensioning, reduction of baseline energy-consumption by updating the PLC and minimisation of peak loads by energy management is engineered. Finally the financial impact of the obtained energy savings is quantified by evaluating the developed methodology during several use cases.

Artificial neural networks and physical modeling for determination of baseline consumption of CHP plants

An effective modeling technique is proposed for determining baseline energy consumption in the industry. A CHP plant is considered in the study that was subjected to a retrofit, which consisted of the implementation of some energy-saving measures. This study aims to recreate the post-retrofit energy consumption and production of the system in case it would be operating in its past configuration (before retrofit) i.e., the current consumption and production in the event that no energy-saving measures had been implemented. Two different modeling methodologies are applied to the CHP plant: thermodynamic modeling and artificial neural networks (ANN). Satisfactory results are obtained with both modeling techniques. Acceptable accuracy levels of prediction are detected, confirming good capability of the models for predicting plant behavior and their suitability for baseline energy consumption determining purposes. High level of robustness is observed for ANN against uncertainty affecting measured values of variables used as input in the models. The study demonstrates ANN great potential for assessing baseline consumption in energy-intensive industry. Application of ANN technique would also help to overcome the limited availability of on-shelf thermodynamic software for modeling all specific typologies of existing industrial processes.

Application of the LEED PRM to an Italian Existing Building

ANSI/ASHRAE/IESNA 90.1 Energy Standard for Buildings except Low-Rise Residential Buildings is the benchmark code of U.S. local regulations. In addition to provide a set of minimum mandatory requirements for energy-using components (prescriptive approach), the standard includes in the Appendix G a more flexible path to attest performances significantly better than code minimum (performance-based approach). With regard to this approach, a procedure to establish the proposed building performance by quantifying the energy savings above an annual baseline energy consumption is defined as Performance Rating Method and used within the Leadership in Energy and Environmental Design (LEED) Protocol.This paper applies the U.S. performance-based approach to the global energy efficiency goal within the prescriptive-oriented Italian framework. In particular, the Performance Rating Method – partially customized to the Italian context by GBC Italia® and in compliance with LEED Italia requirements – has been applied to an Italian case study in order to highlight strengths and weaknesses correlated to a steady adoption of the performance approach in Italian energy policies. The proposed building performance of the case study has been evaluated throughout a dynamic simulation analysis carried out by means of the EnergyPlus code.

노후 고등학교 건물의 에너지효율화 리모델링을 위한 요소기술의 성능 평가

Many countries over the world have acknowledged the global warming problem by greenhouse gas emission and tried to solve the problem. The Korean government has also taken many actions such as The Act on Low Carbon, Green Growth and on Promoting Green Building in that architectural building section takes 1/4 of national greenhouse gas emission. Under the situation that buildings constructed 15 years ago when insulation standards were reinforced take about 74%, The Plan on Vitalizing Green Remodeling, finally established on July 2013, will induce energy-efficient remodeling of deteriorated buildings. Using the energy simulation by the Visual DOE 4.0 program, this paper proposed the ways of energy-efficient remodeling of deteriorated high school buildings by measuring energy saving performance of factors that were drawn from the previous study. The factors considered are insulation, window`s SHGC, south louver, system efficiency, and indoor setting temperature. Among them, all factors except SHGC proved contribution to reducing energy use at deteriorated high school buildings, compared with the baseline energy consumption.