Estimated Energy Savings Research Articles

Exploring building performance evaluation methods to estimate energy savings following field implementation

Research on energy conservation measures and advanced control strategies is becoming increasingly present to improve building energy performance and may reach the stage of field testing in existing buildings. Therefore, it becomes critical to properly assess the benefits following on-site implementation. Different approaches have been conducted in the past to predict building energy performance and evaluate energy savings. However, it remains not clear how the calculation methods and their underlying assumptions eventually influence the savings estimates. This paper aims to compare the energy savings obtained following the implementation of data-driven measures in an existing building and estimated using three different approaches that are commonly found in the literature. The intricacies of these baseline model-free and baseline model-based methods were investigated, and various aspects were explored such as weather normalization, baseline modelling time-step (monthly, daily, hourly, sub-hourly), input sets (including weather, time-index variables, room air temperatures, heating system conditions), and techniques (linear, multi-linear, gaussian process regression). Results show that savings estimates can significantly vary from one performance evaluation method to another, and from one model to another, highlighting the importance of properly selecting both the evaluation method and the baseline model. Baseline model-based approaches were found more appropriate than the other methods, and daily linear regressions against outdoor air temperature and hourly gaussian process regression using weather and time-index variables were found suitable for building performance evaluation.

APPLICABILITY OF THE "TYPOLOGY OF RESIDENTIAL BUILDINGS IN BOSNIA AND HERZEGOVINA", AT A MUNICIPALITY OR CITY LEVEL, CASE STUDY - SINGLE-FAMILY HOUSES IN THE CITY DERVENTA

<p class="ABSTRACT">In order to make correct estimates of energy consumption for heating, reduction of energy consumption, and CO2 emissions by applying energy efficiency measures, it is necessary to know the structure and characteristics of the building stock. The Typology of residential buildings in Bosnia and Herzegovina identified the structure and characteristics of residential building stock through different periods at the level of Bosnia and Herzegovina. This paper analyses the differences between national and city or municipal typology. Conducted analysis for the City Derventa, shows that in the case of single-family houses, the application of national instead of local typology can result in certain deviations in the estimation of energy consumption, savings, and CO2 emissions.</p>

Measured performance of energy efficiency measures for zero-energy retrofitting in residential buildings

Retrofitting existing buildings to Nearly Zero Energy Buildings (NZEBs) can significantly reduce their energy consumption and environmental impact. However, practitioners and policymakers still face the challenge of providing effective and economic energy efficiency measures (EEMs) to interested parties, due to the limited data on the actual performance of available EEMs. This study addresses the data deficiency by analyzing the actual energy and cost performance of a retrofitted single-family house towards a NZEB. Itemized electricity consumptions, including power consumption of heat pump, water pump, fan, and other accessories, as well as whole building usage, are measured before and after the renovation. New metrics are devised for effectiveness assessment – estimating energy savings upon degree day, cost and change in envelope thermal resistance R-value. The contribution of each EEM to the achieved energy savings is identified and compared. For energy consumption of air-water source heat pump, exterior wall retrofit yields the greatest savings: 1.02 kWh/dd (22.2 %) in winter and 0.64 kWh/dd (33.1 %) in summer, followed by roof retrofit. When the savings are analyzed in terms of per unit increment of R-value, windows/doors retrofit demonstrates the highest priority: 1.66 kWh/dd per unit increment of R-value in winter and 0.62 kWh/dd per unit increment of R-value in summer. Considering costs and energy saving contribution together, the optimal retrofit measure is roof retrofit. This study also contributes to the topic by adding new data to the field and extending retrofit practices to locations outside of North America and Europe.

Market instrument for the first fuel and its role in decarbonizing Indian industrial production

India introduced Perform, Achieve, and Trade (PAT) scheme in 2012; it assigns targets to energy-intensive industries with estimated energy savings of 11–16% to enhance energy efficiency and allows energy-saving certificates trading. We qualitatively review India's energy efficiency policy landscape, including the institutional structure, operational trajectory, and outcomes of the PAT scheme's completed cycles. We identify four research themes: 1) Industry-wise specific energy consumption correlation with various factors of the industrial sector under PAT should be identified; 2) PAT scheme impact on aluminium, chlor alkali, textile, etc. Lacks exploration; 3) Realistic baseline target setting should be done for designated consumers under PAT scheme, and 4) machine learning based monitoring and verification for PAT scheme is still nascent but appears promising. We find a lack of ambition in the targets, which are easily met and could harm future investments in energy efficiency.

Impact of time resolution on estimation of energy savings using a copula-based calibration in UBEM

Urban Building Energy modelling (UBEM) has emerged in the last decade as an important tool to accelerate energy transition in the building sector. To fulfil one of its major purposes - forecasting energy savings for potential energy conservation measures at an urban scale, several challenges are still to be addressed. Two key challenges include: 1) the need to calibrate models with a large number of unknown parameters across numerous buildings (either individually or through archetype representation); 2) the limited availability of high-resolution measured data, which raises concerns about calibrated models based solely on yearly values for accurate energy savings forecast. This study addresses these challenges using a case study of 35 buildings in a district in Stockholm, Sweden. Firstly, a new iterative Approximate Bayesian Computation (ABC) method for calibration is proposed, incorporating a copula-based sampling process at each iteration. Secondly, the new calibration process is applied with three different time resolutions to examine the impact of data resolution on forecasted energy usage, particularly when applied to a classical energy conservation measure. Results demonstrate the efficiency of the new method across the 35 buildings, facilitating the rapid population of a final joint distribution for the nine unknown parameters considered in each building. While the marginals may be strongly influenced by the time resolution, the forecasted energy consumption remains identical across the three analysed time resolutions. However, a noticeable difference is observed when the ECM pertains to a formerly unknown or known parameter.

Energy Performance Analysis of the Renovation Process in an Italian Cultural Heritage Building

Renovating buildings with cultural heritage significance is an important step toward achieving sustainability in our cities. The benefits are not only energy-related but also encompass social aspects that make these renovations a high apriority. The present work investigates the renovation process of a cultural heritage building in the Municipality of Trento in Italy, specifically focusing on achieving energy savings and renewable energy integration by implementing various renovation actions. These renovation actions include improvements to the building envelope, such as roof insulation and window replacements. Additionally, the renovation actions for active systems involve the installation of a ground-source heat pump for heating/cooling coupled with a borehole thermal energy storage system, which is an innovative technology for the renovation of cultural heritage buildings. The electrical systems of the building are upgraded through the addition of standard rooftop photovoltaics, innovative building-integrated photovoltaics (shingles), and the installation of an LED lighting system. The baseline and the renovation scenarios are studied using the dynamic simulation tool INTEMA.building, written in the programming language Modelica. This tool simulates both the building envelope and the energy systems with a high level of detail, using advanced control systems and adjustable time steps. According to the simulation analysis, the primary energy demand is reduced by 30.49%, the final energy demand by 36.74%, and the net electricity demand by 8.72%. Results from this study can be useful to interested stakeholders (e.g., building owners, architects, construction companies, public agents, and urban planners) dealing with the renovation of cultural heritage and protected buildings. Also, the results can be exploited for estimating energy savings by applying advanced renovation strategies for cultural heritage buildings.

The inscrutable baseline and the problem of attribution

This paper examines the problem of attribution in the evaluation of energy efficiency program impact. The methodological problem concerns the observability of consumer behavior under the baseline condition of no program intervention. The statistical solution to the problem, which entails randomized exposure of targeted individuals to program influence, is not a viable alternative in most applications. Randomized opt-in and randomized encouragement designs do not conform to this requirement because all targeted individuals are encouraged to participate in the program, resulting in negative exposure bias. Quasi-experimental methods which utilize non-targeted individuals or targeted nonparticipants as baseline surrogates are further subject to selection bias of unknown magnitude and direction. Valid attribution in the general case of unrestricted eligibility depends on prior knowledge of the determinants of measure adoption and program participation. In default of such knowledge, evaluators must rely upon structural assumptions that have no foundation in empirical science. On the other hand, established measurement and verification methods which exploit scientific knowledge of the determinants of end-use energy consumption should be utilized to obtain unbiased estimates of individual measure and gross program energy savings.

Grey-box modeling of Air Handling Units for Analysis and Virtual Sensing

The optimization of control sequences in air handling units (AHUs) presents a significant opportunity for energy savings within HVAC systems. However, many building owners and operators require quantifiable estimates of potential energy savings before committing to retrofitting control systems. Valid estimates of energy savings require system models that consider capacity and limitations of the AHU, but in existing systems, scarce information hinders such modeling efforts. This lack of information complicates AHU modeling and the assessment of alternative control strategies. This paper demonstrates an approach that leverages time-series data from a newly constructed Danish AHU, equipped with multiple sensors for temperature, flow, and pressure, to construct a grey-box model of the unit, including component properties. The estimated parameters for the components are validated against data sheet information, and shows that the estimation procedure is accurate for parameter estimation. To analyze the energy-efficiency of the cooling coil, the model is used to estimate the latent cooling in the cooling coil, as Danish conditions rarely require dehumidification of supply air.

Baseline energy modeling for improved measurement and verification through the use of ensemble artificial intelligence models

Accurate estimation of energy savings is crucial for the effective implementation of energy conservation measures (ECMs). Simultaneously, the integration of Artificial Intelligence (AI) has revolutionized software engineering by imbuing software with intelligent capabilities and autonomy. In this paper, we propose an ensemble model for precisely estimating baseline energy consumption within the realm of AI-empowered autonomous software. The ensemble model combines predictions from three tree-based Machine Learning models, namely Random Forest, XGBoost, and LightGBM. Notably, our model emphasizes the provision of explainability, granting transparency and insights into the key factors influencing baseline energy consumption. To validate its effectiveness, we conduct experimental evaluations on a diverse cluster of real-world buildings in Latvia. The results demonstrate the superiority of our proposed ensemble model over individual models and even a deep learning network tailored for energy consumption estimation. These findings underscore the efficacy of AI-empowered models in the energy sector, offering a robust and interpretable solution for estimating energy savings.

An Automated Machine Learning Approach towards Energy Saving Estimates in Public Buildings

Reducing the energy consumption of buildings in the public sector is an important component in our efforts towards reaching our sustainability goals. In this context, a decisive prerequisite for administrations and policy makers is a tool for estimating the effectiveness of measures to reduce energy consumption. Estimating the impact of planned investments in building technology at scale, however, remains challenging, mainly for two reasons. For one, accurate physical modeling requires detailed building data, which can be difficult to obtain. Second, adapting established building models to novel measures aiming at energy consumption reduction is difficult. Hence, modeling building consumption patterns after retrofitting is a non-trivial task, and more research is needed to improve modeling techniques as well as to assess their effectiveness across a wide range of application scenarios. Modeling tools need to be generic enough to enable modeling of a variety of building types, they should ideally require as few input features as possible and they should allow for a high degree of automation in the selection and calibration of building modeling tools. Here, we propose a novel machine learning approach that does not require detailed building data and can automatically adapt to retrofitting measures. We evaluate our method on a data set of 113 public buildings in 4 building categories in Berlin, Germany. The data set contains energy consumption data in the initial state and after implementation of a weather-predictive heating control system. Despite being fully automated and requiring only minimal information about the building, our model can reliably predict the energy consumption of large public buildings better than established methods. All code and data are publicly released.

Modeling User Transfer During Dynamic Carrier Shutdown in Green 5G Networks

The energy consumption of the fifth generation (5G) of cellular technology is concerning for the mobile industry and the entire society. To minimize the environmental footprint and economic costs of 5G, it is necessary to adapt the transmission capabilities of networks to end-users’ quality of service requirements. In this paper, we focus on the carrier shutdown approach that enables a base station (BS) to autonomously switch off during low traffic periods, by transferring its load to neighbouring active BSs. More specifically, we propose a data-driven framework, constructed through real network measurements, which statistically characterizes the user equipment (UE) transfer across neighbouring BSs, when carrier shutdown operates. The implementation of this framework allows the 5G system to determine a poor load distribution due to energy saving mechanisms, prevent drastic reductions in UE performance, and ultimately estimate energy savings when activating carrier shutdown.

Analysis of Energy Recovery from Municipal Solid Waste and Its Environmental and Economic Impact in Tulkarm, Palestine

Municipal solid waste management (SWM) is one of the most useful applications of renewable energy, supporting the market for renewable energy and assisting in environmental protection by reducing emissions. Sustainability is one of the key issues with solid waste management systems in developing countries, including Palestine. Waste-to-energy (WTE) technology is one of the greatest strategies for effectively managing solid waste. This study focuses mostly on waste management in Palestine’s Tulkarm area. We conducted an evaluation of the potential of energy recovery from municipal solid waste through a number of approaches, such as incineration, gasification, anaerobic digestion, and landfilling. Additionally, the implications on the environment, human health, and economics were investigated. The results showed that anaerobic digestion recovered around 5156.15 kWh per day; whereas, incineration and gasification recovered about 40,986.60 and 14,663.88 kWh per day, respectively. The least recovered energy amount was roughly 3563.87 kWh per day, which was generated by landfilling technology. Additionally, the environmental analysis showed that anaerobic digestion could save around 48,362 tons of CO2 annually; meanwhile, incineration and gasification could save about 384,424 and 137,538 tons of CO2, respectively. The lowest quantity of emissions could be saved by landfilling, with about 33,427 tons saved yearly. The energy cost savings associated with each technology were also estimated. The results of the energy savings estimation showed that incineration had the highest saving of USD 4918.12; gasification and anaerobic digestion saved USD 1759.67 and USD 618.74, respectively. Whereas, landfilling had the lowest saving of about USD 427.66. A careful analysis of all aspects related to each technology, in comparison to the available waste treatment techniques, was carried out to propose a solution to cover the energy deficit in Tulkarm as a primary goal. Anaerobic digestion was found to be more environmentally advantageous and economically feasible and thus can be recommended to decision-makers and investors.

Smart Energy Performance Assessment Platform and next generation Energy Performance Certificate of buildings

After 10 years of track record, the current Energy Performance Certificate (EPC) schemes across the EU face several challenges which have led to not fully accomplish their initial objectives: lack of accuracy, a gap between theoretical and real consumption patterns, absence of proper protocols for inclusion of smart and novel technologies, little convergence across EU schemes, lack of trust in the market and very little user awareness related to energy efficiency. The objective of this paper is to show a holistic methodology for energy performance assessment and certification of buildings that can overcome the above mentioned challenges. The paper presents a Smart Energy Performance Assessment Platform which makes use of the most advanced techniques in dynamic and automated simulation modelling, big data analysis and machine learning, inverse modelling for the estimation of potential energy savings and economic viability check. The reliability, accuracy, user-friendliness and cost-effectiveness of the methodology is checked and validated through 15 case studies in 5 European countries. Additionally, a new useful, enriched and quite user-friendly Energy Performance Certificate format is presented which has been accepted by various target groups, i.e., building users and building related professionals, policy makers. This way, a universal web calculation platform is developed which, together with a new EPC template strongly support the EU goals for more reliable, uniform, and informative EPCs.

Utilizing machine learning models to estimate energy savings from an industrial energy system

Energy audits are an important part of reducing energy usage, costs, and carbon emissions, but there have been discrepancies in the quality of audits depending upon the auditor, which can negatively affect the impacts and credibility of the energy assessment. In this paper, historical energy auditing data from a U.S. Department of Energy sponsored research program was gathered and analyzed with a machine-learning algorithm to predict demand savings from a compressed air system assessment recommendation in industrial manufacturing facilities. Different energy auditors calculate savings for repairing leaks in compressed air systems in various ways, so the energy demand savings have been calculated differently throughout the historical assessment recommendations. Machine learning models are utilized in order to enhance the accuracy of the existing practice and reduce variations resulting from the abovementioned discrepancies. A large set of historical assessment recommendation data was used to train five unique machine learning models. Four base learner models and one metalearner model were devised and compared. Results showed that the distributed random forest model best predicted compressed air energy demand savings against the new scenarios within an error of 17%. This indicates that the distributed random forest model can more accurately quantify savings from repairing leaks in compressed air systems. In addition, the results from this study provide insight into the important factors contributing to leaks in the compressed air systems and why it is crucial to repair those leaks regularly to save money and energy while decreasing emissions.

Methodology for energy savings estimation, resulting from the development of a procedure for air pressure check in vehicle tires

The presented paper examines the possibilities of implementation of a methodology for evaluation of the energy savings, as a result of developed procedure for checking the air pressure in vehicle tires. The purpose of the developed methodology is to provide an opportunity to estimate energy consumption after checking the air pressure in the vehicle’s tires. The methodology is based on numerous experimental studies and analytical evaluation of fuel consumption as a function of tire pressure in vehicles. The conditions, the practical procedures and the sequence in the assessment of energy savings, after the introduction of the methodology for checking the air pressure in the tires of road vehicles, are defined. The methodology applies to road vehicles with a maximum mass of up to and over 3.5 tons with a nominal air pressure in the tires according to the manufacturer’s specifications. The data is summarized, based on the type of transport, number of vehicles, purpose of use - personal and business vehicles, tire pressure check, established average fuel consumption (gasoline, diesel and LPG) and average mileage of the study. Finally, a computational example is presented, proving that the methodology gives correct results for the evaluation of energy savings, after checking the air pressure in the tires of the vehicle.

DEFINITION AND IMPLEMENTATION OF AN ENERGY METERING PLANS FOR ENERGY EFFICIENCY

Fossil fuels are responsible for global warming (carbon emissions). Throughout the world, energy efficiency is deployed in an attempt to reduce this negative impact. Energy management is one lever of energy efficiency. An energy management can’t be successful without a clear and robust metering/monitoring plan. An energy metering plan has two main goals. The first goal is to continuously improve the performance. The second goal is the reduction of carbon emissions and costs. A deep sub-metering permits to realize more energy savings. Estimates of energy savings have ranged from 1% to 20%, depending on the application of the metering systems. This paper contains 2 cases study of metering plan: one in an industrial and one in a residential building.

Estimates of energy savings from energy efficiency improvements in India using Index Decomposition Analysis

Energy efficiency plays a central role in climate change mitigation policies. Countries are using different methods to estimate energy savings from energy efficiency improvements. Improved methods to measure energy efficiency savings are adopted mainly in countries with mandatory energy efficiency targets. They use bottom-up and top-down methods to enhance the reliability of savings estimates. India has implemented various energy efficiency measures, and the impact of most of the schemes is estimated using the simplified deemed savings bottom-up approach. Index Decomposition Analysis is a top-down approach, but its use in India is limited due to a lack of reliable national energy balances. This study uses India’s energy balances from International Energy Agency to estimate the energy efficiency savings in India during 2011–19. It is found that the IDA estimates are lower than the government’s deemed savings estimates. The underlying assumptions in the simplified deemed savings approach may overestimate savings, or savings from index decomposition analysis may be underestimated due to data limitations. Improved methods and reliable and timely data is essential for measuring energy savings from energy efficiency. National-level targets on energy efficiency can push improvements in energy savings measurement techniques. The targets can also address several barriers in the energy efficiency markets.

Full characterization of building energy factor significance by novel integrated stochastic level-based sensitivity analysis with support vector network and multivariate clustering

Effective planning and targeted implementation of building energy efficiency programs explicitly require robust knowledge on significance pattern of involving factors for precision model-based estimates of potential energy savings. Though extensively adopted for factor significance characterization, prior sensitivity analysis (SA) frameworks fail to capture typical influences attributed to commonly used categorical factors. Moreover, they often assume the assessed factors are independent which can be invalid in many cases to generate misleading outcomes. To overcome these observable defects, this paper develops a novel integrated stochastic level-based sensitivity analysis approach for exhaustively patterning effectual significance of all available building factors of varied type while incorporating diverse factor interactions on validated reliability. While the integration of categorical factors is achieved through factor-type dependent variation levelling, the random factor interactions are apprehended by stochastic sampling and nonlinear support vector network computing to accommodate collinearity trap. Multivariate clustering identifies significance pattern via a composite multi-statistic sensitivity metric derived from stochastic energy responses. Case results of 289 U.S. residential houses validate its utility. This presented approach benefits to perform reliable full sensitivity investigations and analyses under collinearity for building energy systems with verifiable robustness.

Estimating energy savings from HVAC controls fault correction through inverse greybox model-based virtual metering

Fault impact analysis is an important phase of fault detection and diagnosis (FDD), enabling prioritization of the faults detected and allocation of resources to procure services and materials. However, many existing fault impact analysis workflows rely on building performance simulation (BPS) models with high engineering cost, limiting their use in FDD software. To this end, this paper introduces an inverse greybox model-based fault impact analysis method using building automation system (BAS) trend data from variable air volume (VAV) air handling unit (AHU) systems to quantify the heating and cooling energy impact of control system faults. It trains inverse models of a VAV AHU system by employing a sequential parameter estimation algorithm, formulates virtual meters quantifying the sensible heat transfer rate from AHU heating and cooling coils and the perimeter heating devices, and employs the virtual meters to estimate heating and cooling energy use of an existing VAV AHU system and the same system after fault correction. The method was used with a synthetic BAS dataset generated by using an EnergyPlus model of a 26-zone VAV AHU system in Ottawa, Canada. The results indicate that the virtual meters could estimate the monthly energy use for heating and cooling at a coefficient of variation of the root mean squared error (CV(RMSE)) of 4–6%. The method was demonstrated with measured BAS data from a 72-zone VAV AHU system, revealing an energy savings potential of 43% for heating and 22% for cooling through fault correction.

Estimates of Long-Run Energy Savings and Realization Rates from a Large Energy Efficiency Retrofit Program

Estimates of Long-Run Energy Savings and Realization Rates from a Large Energy Efficiency Retrofit Program