Energy Performance Monitoring Research Articles

A high-fidelity digital twin predictive modeling of air-source heat pump using FCPM-SBLS algorithm

This paper proposes an innovative Digital Twin (DT) model to predict air-source heat pumps' (ASHP) coefficient of performance (COP) using a combination of fuzzy (c + p) means clustering (FCPM) and the stacked broad learning system (SBLS) algorithm (FCPM-SBLS). The SBLS algorithm is adopted to establish the structure of the DT model, considering the imperative of tracking real-time performance changes in ASHP applications. To enhance the fitting and generalization ability of the DT model, FCPM is employed to partition the dataset into subclasses, allowing for the creation of sub-models specifically tailored to each subclass. Moreover, an incremental-full evolution strategy of the DT model is proposed to track changes in ASHP performance. The DT model's validation is conducted using data collected at a railway passenger station. Results demonstrate that the FCPM-SBLS method has commendable predictive performance, with respective mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE) values of 0.045, 0.012, and 0.252. Additionally, the proposed evolution strategy ensures timely updates for tracking ASHP performance changes. Through analysis of the evolution performance of DT model straddling over years, the updated model shows a 63.1% decrease in MAPE compared to the non-updated model. This study is not only conducive to advancing DT modeling in the field of HVAC systems, but also has practical significance for improving the energy efficiency and performance monitoring of ASHP.

Corrigendum to “Building energy performance monitoring through the lens of data quality: A review” [Energy Build. 279 (2023) 112701

Corrigendum to “Building energy performance monitoring through the lens of data quality: A review” [Energy Build. 279 (2023) 112701

Simulated and monitored electrical energy balance in a solar house in Dubai, UAE

ABSTRACT This paper presents the energy performance simulation and monitoring of a solar powered house designed, built, and operated under Dubai's hot and humid climate, stressing the balance between the electricity consumption and the production of the roof PV system. The study shows a summary of the design process aimed at optimizing the house’s energy performance and maximizing the direct use of solar energy in the specific UAE climatic scenario, which implies a consistent use of cooling throughout the year. The simulated and monitored data collected by a third-party organization grants the reliability of the analysis on the real case study. The comparison of the predicted and monitored PV production shows a moderate gap, primarily due to the variable climatic conditions, but ultimately confirms the efficiency and feasibility of the system.

FedraTrees: A novel computation-communication efficient federated learning framework investigated in smart grids

Smart energy performance monitoring and optimisation at the supplier and consumer levels is essential to realising smart cities. In order to implement a more sustainable energy management plan, it is crucial to conduct a better energy forecast. The next-generation smart meters can also be used to measure, record, and report energy consumption data, which can be used to train machine learning (ML) models for predicting energy needs. However, sharing energy consumption information to perform centralised learning may compromise data privacy and make it vulnerable to misuse, in addition to incurring high transmission overhead on communication resources. This study addresses these issues by utilising federated learning (FL), an emerging technique that performs ML model training at the user/substation level, where data resides. We introduce FedraTrees, a new, lightweight FL framework that benefits from the outstanding features of ensemble learning. Furthermore, we developed a delta-based FL stopping algorithm to monitor FL training and stop it when it does not need to continue. The simulation results demonstrate that FedraTrees outperforms the most popular federated averaging (FedAvg) framework and the baseline Persistence model for providing accurate energy forecasting patterns while taking only 2% of the computation time and 13% of the communication rounds compared to FedAvg, saving considerable amounts of computation and communication resources.

Assessment of energy performance and GHG emissions for the urban water cycle toward sustainability

Abstract This study presents a holistic approach to evaluate energy performance and greenhouse gas (GHG) emissions from urban water supply and sanitation stages, which are important for sustainable water management and climate change mitigation. The study was conducted for Antalya city of Turkey to compare baseline and improved scenario conditions using the Energy Performance and Carbon Emissions Assessment and Monitoring (ECAM) tool. The current application of urban water and wastewater services was defined as the baseline scenario. For the improved urban water cycle, the reduction of non-revenue water, onsite sanitation prevention, increase in energy efficiency, biogas production and reuse of treated wastewater were investigated. Water supply and sanitation stages contributed to approximately 26 and 74% of total GHG emissions and 70 and 30% of energy consumption for the baseline scenario, respectively. GHG emissions were determined approximately as 52,423 tCO2eq/year for CO2 (40%), 47,029 tCO2eq/year for CH4 (35%) and 33,006 tCO2eq/year for N2O (25%) for the baseline scenario. The total GHG emissions of 132,457 tCO2eq/year and energy consumption of 136,328 MWh/year were reduced by 27.65% for GHG emissions and 16.48% for energy consumption for the improved urban water cycle. The outcomes of this research are expected to achieve sustainable cities and combat climate change.

Blockchain + IoT sensor network to measure, evaluate and incentivize personal environmental accounting and efficient energy use in indoor spaces

Electric demand flexibility in buildings is highly dependent on occupant behavior. Evaluating and incentivizing these behaviors can provide grid-responsive support and encourage demand response (DR) participation. To achieve these goals, we developed an infrastructure for connecting Internet of Things (IoT) sensors to a distributed ledger (blockchain network) for long-term monitoring of energy and environmental performance. This study presents a novel Blockchain + IoT paradigm for the building science research community, applied in a real-world application. This Blockchain + IoT Network (BIN) uses Raspberry Pi minicomputers as platforms for connecting sensors to a blockchain network, to provide and analyze real-time indoor environmental quality (IEQ), energy, and carbon intensity data. As part of the study, we propose various metrics to evaluate the environmental footprints of building users. Novel algorithms for normalizing energy usage and carbon intensity, with consideration of a variety of related environmental factors, are executed as smart contracts on the blockchain network. All measurements and the smart contract transactions are reported and visualized on live dashboards. The use of smart contract allocates tokens based on the reward algorithms to incentivize individuals’ energy conservation, and similarly to DR pricing, can help influence occupant consumption patterns towards carbon reduction goals. We further test the smart contract’s algorithm in relation to real sensor data we have collected in two case studies: single-unit households and carbon intensity in the energy market. The combination of proposed metrics translates measured sensor data into token awards, demonstrates upper and lower limits dictated by the grid generation mix profile, and indicates that there is the potential for load shifting to minimize carbon emissions without considering the scale of consumption.

A methodological framework to benchmark and monitor energy performance in textile wet processing small and medium enterprises: Proposal and evaluation

The customers of textile wet processing plants have committed to reduce their greenhouse gas emissions from outsourced manufacturing activities. The textile brands directly use specific energy consumption values to analyze and set steep, time-bound emission intensity reduction targets for the energy intensive textile wet processing plants. However, the direct use of specific energy consumption values to evaluate energy savings can be misleading. In this paper, the authors propose a methodological framework to systematically evaluate energy performance and monitor energy savings in the textile wet processing plants. Further, the approach was extended to analyze the energy performance monitoring practices and identify energy conservation measures. The proposed framework was developed based on thorough analysis of the manufacturing process of textile wet processing plants and the principles of ISO 50001. A textile wet processing plant was chosen as a case study for illustrating the effectiveness of the framework using multiple regression method. Bottom up approach for benchmarking was used and yet significantly simplified by the introduction of an innovative parameter “program mix factor” and thereby evaluate “equivalent specific energy consumption”. The predicted values for specific energy consumption were found to be within a close error range of ±10 % of the actual data. The proposed methodological framework is therefore innovative, simple and yet comprehensive to conduct internal benchmarking in textile wet processing plants. The textile brands are suggested to use equivalent specific energy consumption instead of specific energy consumption for energy performance monitoring of their textile wet processing plant suppliers. The authors also conducted a survey in fifteen textile wet processing plants to assess the prevailing energy monitoring practices. The results of the survey suggests that only 20 % of the plants are practicing basic level energy performance monitoring, while the remaining 80 % are at minimal level.

Insight into Greenhouse Gases Emissions and Energy Consumption of Different Full-Scale Wastewater Treatment Plants via ECAM Tool.

Greenhouse gas (GHG) production is one of the urgent problems to be solved in the wastewater treatment industry in the context of “carbon neutrality”. In this study, the carbon emissions and energy consumption of typical wastewater treatment processes in China were evaluated, starting from different cities and water treatment plants. Tool of Energy Performance and Carbon Emission Assessment and Monitoring (ECAM) was used. By comparing the influent BOD5, it was found that the energy consumption for wastewater treatment was positively correlated with the influent organic load. The annual CH4 emission of Xi’an WWTP can reach 19,215 t CO2eq. Moreover, GHGs are closely related to the wastewater treatment process chosen. WWTP B of Kunming used only an anaerobic process without continuous aeration, with an average monthly energy consumption of 8.63 × 105 kW·h. The proportion of recoverable biogas was about 90% in the GHG discharged by the traditional process. However, the anaerobic digestion-thermoelectric cogeneration process can make the recovery of the biogas utilization ratio reach 100%. Compared to the Shuozhou WWTP and WWTP A of Kunming, the Strass WWTP served the smallest population and had the largest treatment capacity, reaching the lowest energy consumption, consuming only 23,670 kW·h per month. The evaluation and analysis of ECAM provide data support and research foundation for the wastewater treatment plants to improve energy utilization and reduce greenhouse gas emissions.

Energy smart management and performance monitoring of a NZEB: Analysis of an application

The NZEB (Nearly Zero Energy Building) spreading with its reduced energy performance is one of the main purposes of the Directive 2010/31/EU.New buildings design and existing buildings renovation have been the subjects of discussion to trace the way towards achieving a high level of energy performance of the entire European building stock. However, in order to keep long-term optimal results, it is necessary to plan a correct management both of thermal systems and of passive climate control.There is still a small amount of data available about the effectiveness of the measures adopted for energy saving in the new design to demonstrate the maintaining of the performance. Therefore, it could be useful to broaden experimental monitoring campaigns, in order to better address the design choices in the next future.As a contribution to increasing knowledge about the intelligent management of an NZEB, the control systems and operating procedures of thermal plants of a case-study (new single house building) are discussed, based on simulation results and long-term measurements. The control procedures for internal environmental conditions and plant systems, aiming at maintaining both indoor comfort conditions and an efficient management of energy consumption, are described.The analyses quantitatively demonstrate how the correct energy management can significantly reduce the actual consumption, compared to the estimated one. Moreover, the need of the adaptation of the home automation management systems to the actual use of the building is put in evidence.Their smart use in fact guarantees the expected performance, and in some cases can improve it, by tailoring the energy behaviour of the building to the users’ specific needs.In the case study, the energy performance of the building, calculated by means of the national methodology, satisfies the NZEB target. The long term monitoring reveals its potential as a Positive Energy Building (PEB) because it produces more energy than its needs.The important support of these analyses shows that it is useful to get a wider number of case studies in order to expand the knowledge of the effective NZEB energy behaviour and to put in evidence smart choices for obtaining the maximum long-lasting energy performance.

Component-level re-commissioning of a newly retrofitted Danish healthcare building

The building sector is among the sectors with the highest energy use, responsible for 40% of the total energy consumption in the EU. The high energy consumption is partly due to inefficient operation and the delayed detection of faulty systems and components in the buildings, which entails both increased operational costs and CO2-emissions. Thus, building energy performance monitoring and evaluation is key in ensuring a proper building operation and timely detection of anomalies and faults on different levels. This can be implemented through systematic building commissioning, a quality-assuring process aiming to ensure that buildings are operating according to the expected performance. While initial commissioning of newly built buildings has been the norm in the last decades, re-commissioning of deeply retrofitted buildings is generally overlooked despite the potential and positive impacts. In this work, a whole-building EnergyPlus model is developed, calibrated, and employed for the re-commissioning of building components in a Danish health care building that has recently exhibited a deep energy retrofit process on various levels including the installation of new ventilation and cooling units. Both the calibration and the commissioning of these components is performed using data collected from non-invasive clamp-on temperature and power sensors. The commissioning process is performed using a limit checking technique with a predefined threshold, allowing the detection of multiple anomalies during the commissioning period.

Retrofitting Existing Buildings to Improve Energy Performance

Energy-efficient retrofits embrace enhancement of the building envelope through climate control strategies, employment of building-integrated renewable energy technologies, and insulation for a sustainable city. Building envelope improvements with insulation is a common approach, yet decision-making plays an important role in determining the most appropriate envelope retrofit strategy. In this paper, the main objective is to evaluate different retrofit strategies (RS) through a calibrated simulation approach. Based on an energy performance audit and monitoring, an existing building is evaluated on performance levels and improvement potentials with basic energy conservation measures. The considered building is experimentally monitored for a full year, and monitoring data are used in calibrating the simulation model. The validation of the base model is done by comparing the simulation analysis with the experimental investigation, and good agreement is found. Three different retrofit strategies based on Intervention of minor (RS1), Moderate (RS2), and Major (RS3) are analyzed and juxtaposed with the base model to identify the optimal strategy of minimizing energy consumption. The result shows that total energy intensity in terms of the percentage reduction index is about 16.7% for RS1, 19.87 for RS2, and 24.12% for RS3. Hence, RS3 is considered the optimal retrofit strategy and is further simulated for a reduction in carbon dioxide (CO2) emissions and payback investigation. It was found that the annual reduction in CO2 emissions of the building was 18.56%, and the payback period for the investment was 10.6 years.

Online Energy Performance Monitoring and Evaluation for Continuous Commissioning in a Danish Office Building

The development, implementation, and evaluation of an online building energy performance monitoring and evaluation ‘ObepME’ platform in a 2600 m2 Danish office building is presented and discussed. A whole building dynamic energy model was developed in EnergyPlus and calibrated using actual onsite data. The calibrated model is then used as a basis for continuous and automated commissioning. A list of performance tests was developed targeting energy consumption on different levels. An online dashboard was created to automatically compare and visualize model simulations and actual building consumption as part of the building continuous commissioning. The model development and calibration along with ObepME implementation are presented in this paper. Major findings from the continuous commissioning platform and examples of malfunctions observed are reported and analysed.

Energy performance measurement, monitoring and control for buildings of public organizations: Standardized practises compliant with the ISO 50001 and ISO 50006

The International Standards ISO 50001 and ISO 50006 can be easily transferred to organizations, which outputs consist of tangible products. However, it is equally important to build an energy management system tailored to public organizations, whose outputs are often immaterial goods and in which buildings highly affect the overall energy performances. Commonly, energy performances of buildings are assessed by comparison with sector-based benchmarks, whilst monitoring and control practises are often overlooked. Under these premises, this paper aims at proposing a common framework for an energy management system tailored to public organizations in which buildings play a pivotal role in targeting energy performance improvement. The proposed energy management system also relies on the effective exploitation of monitoring and control tools to promptly identify deviations from the expected energy performance values and to evaluate improvements over time.

Parametric energy performance analysis and monitoring of buildings—HEART project platform case study

Building performance analysis changed the way in which buildings are designed and operated. The evaluation of different design and operation options is becoming more resource intensive than ever before. Although building dynamic simulation tools are potentially a suitable way for assessing energy performance of buildings accurately, they require adequate training and a careful evaluation of model input data. In Europe, the majority of buildings were constructed before 1990 and are in urgent need for a significant energy efficiency improvement, through deep renovation. In this respect, advanced renovation solutions are available, but costly and lengthy renovation processes and technical complexities hinder the achievement of a large scale impact. Energy refurbishment of buildings is an open challenge and essentially requires the adoption of a valid methodological approach to link design and operational performance analysis transparently, in order to address the potential gap between simulated and measured results.The HEART project, funded in the EU Horizon 2020 program, aims to address the increasing need for deep retrofit interventions and to develop systemic strategies leading to high performance and cost effective solutions. The research for the cloud platform used in the project is based on two fundamental tools: parametric simulation to produce a large spectrum of possible building energy performance outcomes (considering realistically the impact of the user behaviour and variable operating conditions from the very beginning), and model calibration employing simple, robust and scalable techniques. In this paper we present the preliminary development and testing of the computational processes that will be implemented in the cloud platform, employing the first pilot case study of HEART Project in Italy, currently under refurbishment.

An automated framework for buildings continuous commissioning and performance testing – A university building case study

One of the main challenges facing the building sector nowadays is the reported mismatch between the predicted and the actual performance throughout the building operational phase. This mismatch is referred to as the ‘building performance gap’. In this regard, the need for a systematic continuous commissioning framework to monitor, assess and evaluate the buildings performance is vital to bridge the performance gaps. In this paper, an innovative framework for building energy performance monitoring and evaluation is presented, considering a list of performance tests addressing building subsystems. The framework relies on two major pillars, actual data collected from the building site, and calibrated energy model simulations to serve as a dynamic baseline for comparison and evaluation. The framework design, development and implementation in a highly energy efficient building is presented, and findings from the initial stages of implementing the framework are highlighted considering the energy systems operation and indoor comfort perspectives.

Environmental and Energy Assessment of Municipal Wastewater Treatment Plants in Italy and Romania: A Comparative Study

Municipal wastewater treatment plants (MWWTPs) are essential infrastructures in any urban context, but they may be considered as a potential source of greenhouse gas (GHG) emissions and should be coherent with European Union (EU) policy on energy efficiency. This study presents a sustainability evaluation of four Italian and Romanian MWWTPs in terms of energy efficiency and greenhouse gas emissions using Energy Performance and Carbon Emissions Assessment and Monitoring (ECAM) tool software. The obtained results indicated that biogas recovery improved energy performances, while the largest contributions in terms of GHG emissions were in all cases caused by energy consumption and methane produced during wastewater treatment. The Romanian plants exhibited higher GHG emissions, compared to the Italian plants, mainly because of the different values of national conversion factors for grid electricity (0.41 kg CO2/kWh for Italy and 1.07 kg CO2/kWh for Romania). Two scenarios aimed at enhancing the overall sustainability were hypothesized, based on increasing the serviced population or energy efficiency, achieving significant improvements. A sustainability assessment of MWWTPs should be adopted as a useful tool to help water utilities to introduce low-energy, low-carbon management practices as well as being useful for policy recommendations.

Baseline carbon emission assessment in water utilities in Jordan using ECAM tool

Abstract This study presents a baseline assessment of carbon emissions in water utilities in Madaba, Jordan. The Energy Performance and Carbon Emissions Assessment and Monitoring Tool (ECAM) is applied in the present study in order to reduce indirect and direct emissions. Input data for the assessment included inter alia, population, water volumes, energy consumption, and type of wastewater treatment. The methodology focuses on the greenhouse gas (GHG) emissions and energy use that is directly associated with the utility operations covering the whole water cycle. The ECAM's Quick Assessment revealed that 89.7% of the energy is consumed in abstraction and distribution systems of water supply, whereas wastewater collection, treatment, and discharge consumed only 10.3% in Madaba. The detailed ECAM tool assessment results showed that total GHG emissions from the entire water and wastewater system in Madaba are approximately 28.122 million kg CO2/year. The water supply is the major contributor to GHG accounting for 62.4%, while 37.6% of GHG emissions result from sewage treatment, and are associated with treatment process requirements considered in this work, in addition to sludge transport from septic tanks to the wastewater treatment plant. The findings of this work can help the utility to undertake energy efficiency and GHG reduction measures.

A low-cost wireless sensor network for long term monitoring of energy performance and sustainability of buildings

This manuscript describes the development of a wireless sensor system for long term monitoring of temperature, humidity and heat flux reading within building structural elements, including places that are hard to reach using wired sensors. The system was tested in cold Latvian climate in 3 different buildings. The main objectives during the development phase were the maximization of network operational lifetime, ensurance of work stability and maintenance cost reduction to make the system feasible for wide use in practical applications. An optimal radio module and microcontroller combination yielded sufficient signal range and data transfer stability, as well as efficient control of energy consumption.

Intra ELM variants ensemble based model to predict energy performance in residential buildings

Abstract Optimization of energy performance of the buildings has been a great interest of research. Energy performance monitoring and maintenance depend on the several parameters such as temperature, humidity, sunlight, roof area, wall area and mostly on heating ventilation and air conditioning(HVAC). Each building has its own patterns and settings obtained through monitoring which depends on many parameters such as users activities, environmental related attributes and building design structural parameters etc. In this respect, an estimation of energy load of the building in real-time efficiently for optimization becomes an important task for cost-effective energy management. The paper proposes an Intra ensemble model based on variants of emerging machine learning approach which includes extreme learning machine(ELM), Online Sequential ELM(OSELM) and Bidirectional ELM(B-ELM). The energy performance estimation requires the model to be real-time and efficient. This asks for use of highly correlated parameters and a very efficient model. For this, OSELM based model for real-time prediction of energy performance has been used. ELM variants are used because of their fast computation and efficiency of prediction over conventional machine learning models. The proposed model has been compared with few state of art methods on accuracy and efficiency criteria and proposed models outperformed existing methods.

User Centered Neuro-Fuzzy Energy Management Through Semantic-Based Optimization.

This paper presents a cloud-based building energy management system, underpinned by semantic middleware, that integrates an enhanced sensor network with advanced analytics, accessible through an intuitive Web-based user interface. The proposed solution is described in terms of its three key layers: 1) user interface; 2) intelligence; and 3) interoperability. The system's intelligence is derived from simulation-based optimized rules, historical sensor data mining, and a fuzzy reasoner. The solution enables interoperability through a semantic knowledge base, which also contributes intelligence through reasoning and inference abilities, and which are enhanced through intelligent rules. Finally, building energy performance monitoring is delivered alongside optimized rule suggestions and a negotiation process in a 3-D Web-based interface using WebGL. The solution has been validated in a real pilot building to illustrate the strength of the approach, where it has shown over 25% energy savings. The relevance of this paper in the field is discussed, and it is argued that the proposed solution is mature enough for testing across further buildings.