Estimation Of Energy Performance Research Articles

Data-driven performance analysis of an active chilled beam air conditioning system: A machine learning approach for energy efficiency and predictive maintenance

This study aims to enhance energy performance estimation and maintenance planning for office buildings in Tehran. Machine learning and data mining techniques were employed, utilizing Support Vector Machines (SVMs), Artificial Neural Networks (ANNs), Narrow Neural Networks, and optimizable SVMs. A comprehensive dataset of 2500 experimental samples was utilized with five input parameters (building size, occupancy patterns, environmental conditions, and ACB system parameters) and one output parameter (electricity consumption). GPR models performed better in estimating electricity consumption, achieving a Root Mean Square Error (RMSE) of 297.01 and an impressive R-squared value of 0.99. To optimize energy performance and design more energy-efficient buildings, this model accurately predicts energy consumption. Additionally, this study addressed maintenance planning for ACB systems, simulating maintenance issues with 2500 synthetic samples. The dataset included building age, size, number of floors, usage intensity, previous maintenance history, location, and environmental factors. Maintenance time was estimated using these features, and XGBoost was employed to predict ACB system maintenance time. Low MSE, RMSE, and R-squared values in the XGBoost model allow for accurate predictions. Using this model, facility managers can proactively plan maintenance activities, allocate resources, and minimize downtime. Through proactive measures, ACB performance and maintenance costs are optimized. The study accurately predicts ACB system energy consumption and maintenance time using the rational quadratic GPR model, applicable in Tehran and beyond. Additionally, MATLAB version R2022b was used for prediction, while DesignBuilder and EnergyPlus were utilized for technical analysis.

A Neural Network Trained by Multi-Tracker Optimization Algorithm Applied to Energy Performance Estimation of Residential Buildings

Energy performance analysis in buildings is becoming more and more highlighted, due to the increasing trend of energy consumption in the building sector. Many studies have declared the great potential of soft computing for this analysis. A particular methodology in this sense is employing hybrid machine learning that copes with the drawbacks of single methods. In this work, an optimized version of a popular machine learning model, namely feed-forward neural network (FFNN) is used for simultaneously predicting annual thermal energy demand (ATED) and annual weighted average discomfort degree-hours (WADDH) by analyzing eleven input factors that represent the building circumstances. The optimization task is carried out by a multi-tracker optimization algorithm (MTOA) which is a powerful metaheuristic algorithm. Moreover, three benchmark algorithms including the slime mould algorithm (SMA), seeker optimization algorithm (SOA), and vortex search algorithm (VSA) perform the same task for comparison purposes. The accuracy of the models is assessed using error and correlation indicators. Based on the results, the MTOA (with root mean square errors 2.48 and 5.88, along with Pearson correlation coefficients 0.995 and 0.998 for the ATED and WADHH, respectively) outperformed the benchmark techniques in learning the energy behavior of the building. This algorithm could optimize 100 internal variables of the FFNN and acquire the trend of ATED and WADHH with excellent accuracy. Despite different rankings of the four algorithms in the prediction phase, the MTOA (with root mean square errors 9.84 and 95.96, along with Pearson correlation coefficients 0.972 and 0.997 for the ATED and WADHH, respectively) was still among the best, and altogether, the hybrid of FFNN-MTOA is recommended for promising applications of building energy analysis in real-world projects.

Split-degree day method: A novel degree day method for improving building energy performance estimation

Degree days have been widely used in different applications in buildings, such as estimating building energy use and climate classification for building energy standards. However, there are limitations with the use of conventional degree days that result in inaccuracies in estimating building energy consumption using degree day-based methods. This paper proposes a new method, the split-degree day method, that shows substantially improved results in the accuracy of the building energy use estimation compared to the conventional degree day methods. The analysis in 801 locations in the U.S. using regression models showed that the new, proposed split-degree day method in this paper, compared to the conventional degree day method, better accounts for the weather parameters and more accurately estimates end-uses. The split-degree days showed an improvement of over 5% in the accuracy of the total annual energy use prediction, 8% for predicting the heating energy use, 0.3% for predicting the cooling energy use, and 33% for predicting the fan energy use. Additionally, the analysis showed improved results for the model with higher thermal mass and the model with a 24-hour operating schedule. These improvements were due to the fact that the split-degree day method includes more information related to the weather characteristics of a location, which is missing in a conventional degree day calculation procedure that relies on aggregated data at daily intervals. Furthermore, as opposed to the conventional degree days, the accuracy of the estimations using the split-degree days are shown to maintain their high accuracy when the base temperature deviates from the optimal base temperature. The proposed method can be used as a more accurate alternative to the conventional degree days in different applications, including but not limited to building climate classification, building energy estimation, and weather normalization for building energy savings calculation.

Energy performance estimation for large building portfolios with machine learning-based techniques

Building operation is responsible for 28% of the world’s carbon emissions. In this context, establishing priorities in refurbishment strategies at the scale of a city or a group of buildings is important. Such procedures are usually led by experts in energy performance and, therefore, they are rarely carried out due to their long and costly nature.This research aims at the estimation of building energy performance to pave the way towards finding near-optimal refurbishment strategies. Thanks to the identification of easily-accessible building characteristics, the method applies machine learning models to scan a building portfolio based on a low level of details. The results show good potential to identify low-performer buildings with simple machine learning methods. It also opens the door for further improvements through the inclusion of supplementary building features at the input of the predictive system.This work includes (a) the integration of a knowledge database thanks to the Swiss CECB energy performance certificates, referencing more than 70 000 buildings, (b) the preparation of a training data set through the selection of relevant physical characteristics of buildings (input) and the corresponding energy consumption labels (output), (c) the development of predictive models used in a supervised way, (d) their evaluation on an independent test set.

Optimized area efficient quantum dot cellular automata based reversible code converter circuits: design and energy performance estimation

Quantum-dot cellular automata (QCA) based circuit designs are creating a surge in transistorless computational technologies. Due to its quasi-adiabatic switching resulting in extremely low leakage power dissipation as there is no continuous path. These circuits also enjoy extremely high packaging density of the order of 10 $$^{12}$$ devices/ $$\hbox {cm}^2$$ because of its extremely scaled area of 18 nm x 18 nm along with very high 100 GHz frequency of operation. Further the loss of bit information could be abolished by reversible logic computing. This thereby realizes an energy efficient logic operations owing to bijective relation between inputs and outputs in reversible logic. This work investigates the code converter circuits which converts 4-bit binary code to excess-3 code and vice versa based on reversible QCA logic gates for the first time. Moreover an area efficient design for 4-bit binary to gray and vice-versa code converters also designed here. All these four code converter circuits are designed using reversible logic gate Feynman and Peres gates by deploying the QCA Designer and Designer-E tool v2.0.3. Finally the in depth performance estimation of the proposed circuits in terms of circuit complexity, quantum cost and energy dissipation are also presented here. Moreover, these QCA based circuits provide a strong evidence that reversible logic based QCA circuits can be efficiently deployed for these code converter circuits.

A Comparative Study of Artificial Intelligence Models for Predicting Interior Illuminance

ABSTRACT Thanks to the recent advances in computer technology, many building energy performance simulation tools have been developed in the current market. Designers and architects are interested in working on this topic in the early phases of the project. However, effective energy solutions are computationally expensive. As a result, having a comprehensive insight into the project conditions in the early phases of the work is a vital issue. The present study aimed to propose an artificial intelligence (Al) model to generate a reasonably accurate estimate in a short time. To this end, four machine learning models and one artificial neural network (ANN) are selected and their results are compared to assess their capabilities in energy performance estimation. This study investigates the influence of the exterior louver design on the interior energy performance of a structure. A specific dataset is generated and tested on four powerful regression models (i.e., polynomial Linear Regression, Random Forests (RF), Decision Tree (DT), and Support Vector Regression (SVR)) and one Artificial Neural Network (ANN). Finally, a comparative analysis is presented. The findings of this research support the use of machine learning tools and ANNs as a convenient and accurate strategy for predicting building parameters.

Machine learning modelling for predicting non-domestic buildings energy performance: A model to support deep energy retrofit decision-making

Non-domestic buildings contribute 20% of the UK’s annual carbon emissions. A contribution exacerbated by its ageing stock of which only 7% is considered new-build. Consequently, the government has set regulations to decrease the amount of energy take-up by buildings which currently favour deep energy retrofitting analysis for decision-making and demonstrating compliance. Due to the size and complexity of non-domestic buildings, identifying optimal retrofit packages can be very challenging. The need for effective decision-making has led to the wide adoption of artificial intelligence in the retrofit strategy design process. However, the vast retrofit solution space and high time-complexity of energy simulations inhibit artificial intelligence’s application. This paper presents an energy performance prediction model for non-domestic buildings supported by machine learning. The aim of the model is to provide a rapid energy performance estimation engine for assisting multi-objective optimisation of non-domestic buildings energy retrofit planning. The study lays out the process of model development from the investigation of requirements and feature extraction to the application on a case study. It employs sensitivity analysis methods to evaluate the effectiveness of the feature set in covering retrofit technologies. The machine learning model which is optimised using advanced evolutionary algorithms provide a robust and reliable tool for building analysts enabling them to meaningfully explore the expanding solution space. The model is evaluated by assessing three thousand retrofit variations of a case study building, achieving a root mean square error of 1.02 kgCO2∕m2×year equal to 1.7% of error.

Comparative Analysis of Regression Learning Methods for Estimation of Energy Performance of Residential Structures

Energy efficiency is a top priority for private and commercial buildings. This study evaluates the performance of six regression learning methods, including Linear Regressor, MLP Regressor, RBF Regressor, SVM Regressor, Gaussian Processes, and ANFIS Regressor to predict the heating and cooling loads of residential buildings. 768 buildings were considered and analyzed based on the influential parameters, such as relative density, surface area, wall area, roof area, overall height, orientation, glazing area, and glazing area distribution for predicting heating load and cooling load. Three statistical criteria such as correlation coefficient (R), mean absolute error (MAE) and root mean square error (RMSE) were used to assess the potential of the regression methods used in this study. The best estimation results were obtained with the ANFIS regression model, with R of 0.998, MAE of 0.46 and RMSE of 0.68 for HL; and with R of 0.990, MAE of 1.26 and RMSE of 1.60 for CL.

Harvesting big data from residential building energy performance certificates: retrofitting and climate change mitigation insights at a regional scale

The reduction of energy consumption and the increase in energy efficiency is currently an important cornerstone of EU policy. Energy performance certificates (EPCs) were implemented as one of the tools to promote this agenda, and are used for the energy performance assessment of buildings. In this study, the characteristics of the Portuguese dwelling stock are regionally analysed using data from approximately 523,000 Portuguese residential EPCs. Furthermore, a bottom-up building typology approach is used to assess the regional energy needs impact of retrofitting actions and to estimate the heating and cooling energy performance gaps of the whole dwelling stock, as well as the potential CO2 emissions resulting from the gaps’ potential offset due to increase thermal comfort. The results show that Portuguese residential buildings have very low energy performance, with windows and roofs being identified as the most energy inefficient elements. Roof retrofitting has the highest potential for the reduction of energy needs. The estimated heating and cooling energy performance gap amount to very significant percentages, due to the poor performing building stock but also very low energy consumption levels, with probable consequences for the thermal comfort of occupants. Assuming the current energy mix, carbon emissions would be 9.8 and 20.2 times higher associated with heating and cooling, respectively, if the actual final energy consumption were to match the estimated theoretical values derived from building regulation. This study demonstrates several application cases and leverages the potential of the individual EPC, increasing the detail in the dwelling stock characterization and energy performance estimation, revealing its value for energy retrofit and climate change mitigation assessments, as well as establishing the ground for future work related to building retrofits, energy efficiency measure implementation, climate change mitigation, thermal comfort, and energy poverty studies.

Building Blocks Energy Estimation (BBEE): A method for building energy estimation on district level

In this paper, a new method entitled “BBEE (Building Blocks Energy Estimation)” for estimating energy performance at the district level is proposed. It consists of two branches, namely the BBEE algorithm and an energy database of a typical zone. This method is used to keep architects and urban planners informed of the energy performance of their design by clustering zones and looking up corresponding energy intensities from the database. Because the related energy databases are preexisting, energy performance modeling and simulation are not required from the user. On the other hand, the database is set up and continuously replenished by parameterization simulations with a basic model of typical zones. This task was separated from the estimation process and operated by experts, which shortened the process and reduced modeling errors. As a case study, an urban district with eight buildings was estimated under three climate conditions separately following the proposed BBEE method. Results were compared with the full-model simulation using a simulation tool called DeST. The difference in annual heating demand between estimation and simulation was reported as less than 6% in all three cases. The potential applications and limitations of the proposed BBEE method are discussed at the end of this paper. Although further improvements are still required, the new estimation method could be helpful for energy performance estimation at the district level at the early stage of design.

Energy performance of single family houses in Serbia: Analysis of calculation procedures

Energy certification of buildings in Serbia was introduced in 2011 and energy label depends on energy need for heating per unit floor area of heated space, calculated by the fully prescribed monthly quasi-steady-state method defined by ISO 13790. In the Republic of Serbia, most of families live in single-family houses built before the energy certification of buildings was introduced. Therefore, the estimation of energy performance of the existing buildings is important for labeling, and evaluation of energy saving measures and energy strategies to be implemented. This paper examines the applicability of monthly method defined by National legislation on the existing buildings stock in Serbia, by comparing it to the more accurate dynamic simulation method. Typical single-family houses are taken as a test case, since they are responsible for about 76% of energy consumption for heating. The results show that the dynamic simulation method estimates 21% to 54% higher energy need for heating, compared to the monthly method. Also, the monthly method estimates up to 13% higher savings by typical building envelope energy saving measures, compared to the dynamic simulation. This paper recommends improvement in procedures for calculation of building energy performance index to better assess energy consumption, effects of energy saving measures, and create solid background for developing and implementing of energy saving strategies.

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.

METHODOLOGY DEVELOPMENT FOR CREATION OF SCHOOL BUILDINGS TYPOLOGY IN SERBIA

After development of methodology for formulation of residential building typology, the same research team from the Faculty of Architecture University of Belgrade developed and formulated the methodology for typology of school buildings. For this purpose, specific method of data acquisition was designed and conducted. Obtained data represent part of the public buidling database and were further used for defining typology of school buildings, as well as estimation of energy performance of public buidlings within EmBuild project (Horizon2020 framework). In the paper the methodology of creating this database and gathering information, as well as further work on definition and final typology of school buidlings are presented.

Data-driven based estimation of HVAC energy consumption using an improved Fourier series decomposition in buildings

Many data-driven algorithms are being explored in the field of building energy performance estimation. Choosing an appropriate method for a specific case is critical to guarantee a successful energy operation management such as measurement and verification. Currently, little research work on assessment of different data-driven algorithms using real time measurement data sets is available. In this paper, five commonly used data-driven algorithms, ARX, SS, N4S, discretized variable BN and continuous variable BN, are used to estimate HVAC related electricity energy consumption in a university dormitory. In practice, total energy consumption data is easily accessible, while separated HVAC energy consumption data is not commonly available due to expensive sub-metering and/or the complexity of mechanical and electrical layouts. A virtual sub-meter based on a decomposition method is proposed to separate HVAC energy consumption from the total building energy consumption, which is derived from an improved Fourier series based decomposition method.

Evaluation of tree-based ensemble learning algorithms for building energy performance estimation

Tree-based ensemble learning has received significant interest as one of the most reliable and broadly applicable classes of machine learning techniques. However, thus far, it has rarely been used to model and evaluate the drivers of energy consumption in buildings and as such its performance and accuracy in this field have yet to be properly tested or fully understood. The goal of this paper is to evaluate the performance of three ensemble learning algorithms in modelling and predicting the heating and cooling loads of buildings, namely (i) random forests, (ii) extremely randomized trees (extra-trees), and (iii) gradient boosted regression trees. Results show that the tested algorithms outperform the ones proposed in the recent literature, with gradient boosting improving on the prediction accuracy of the second best-performing algorithm by an average of 14% and 65% for the heating and cooling loads, respectively.

Heat Pump System Simulation towards Energy Performance Estimation in Office Buildings

This investigation presents an estimation of the energy performance of an office building which is equipped with a heat pump system. The paper presents the simulation methodology of a multi-zone office building for temperate climate conditions of Belgrade city. Our baseline model was developed according to the guidelines of designing office spaces, accessories and communication. The parameters of the building envelope, heat pump system and indoor environmental criteria were mandatory elements of the simulation. The medium office building model is constructed according to the Serbian energy efficiency regulation, Official gazette RS no. 61/2011. The EnergyPlus simulation results outline the importance of applying simulation techniques in energy performance assessment for qualitative decision-making. Findings highlight the office building's energy performance equipped with an air-to-air heat pump system, with and without heat recovery equipment.

An Incremental Radial Basis Function Network Based on Information Granules and Its Application.

This paper is concerned with the design of an Incremental Radial Basis Function Network (IRBFN) by combining Linear Regression (LR) and local RBFN for the prediction of heating load and cooling load in residential buildings. Here the proposed IRBFN is designed by building a collection of information granules through Context-based Fuzzy C-Means (CFCM) clustering algorithm that is guided by the distribution of error of the linear part of the LR model. After adopting a construct of a LR as global model, refine it through local RBFN that captures remaining and more localized nonlinearities of the system to be considered. The experiments are performed on the estimation of energy performance of 768 diverse residential buildings. The experimental results revealed that the proposed IRBFN showed good performance in comparison to LR, the standard RBFN, RBFN with information granules, and Linguistic Model (LM).

Confirming the Actual Energy Efficiency for Commissioned Buildings

The article provides an overview of the legislative and regulatory base used in the modern system of technical regulations in the Russian Federation in terms of improving the energy efficiency of buildings. The author shows the necessity for the experimental research to find the basis for standard regulations to implement the legislative requirements for actual confirming the building energy efficiency in practice. The results obtained provide a verification method for energy performance estimation based on calculation of the energy performance certificate with the help of factual values of the heat and power indicators.

University campuses energy performance estimation in Ukraine based on measurable approach

Abstract Aim of research is an attempt to create the methodology for University campuses (UC) energy certification system for Ukraine. Therefore, taking into account national conditions, the certification variant, based on the instrumental approach to the UC data as a whole, has been suggested and analyzed. Normalized information on energy consumption has been analyzed based on weather and climatic conditions and operating parameters of UCs and used for UCs clustering based on the Universities’ activity profiles and their specific energy consumption. There was a research conducted of three variants of energy efficiency grading for UCs of Ukraine according to end-use specific heat consumption, electricity consumption, total energy consumption and specific water consumption. The paper demonstrates possibilities and problems of using certification to determine the UCs energy consumption efficiency. The offered approach allows to determine the University campuses energy performance in general and using certification to compare UCs energy efficiency.

Accurate quantitative estimation of energy performance of residential buildings using statistical machine learning tools

We develop a statistical machine learning framework to study the effect of eight input variables (relative compactness, surface area, wall area, roof area, overall height, orientation, glazing area, glazing area distribution) on two output variables, namely heating load (HL) and cooling load (CL), of residential buildings. We systematically investigate the association strength of each input variable with each of the output variables using a variety of classical and non-parametric statistical analysis tools, in order to identify the most strongly related input variables. Then, we compare a classical linear regression approach against a powerful state of the art nonlinear non-parametric method, random forests, to estimate HL and CL. Extensive simulations on 768 diverse residential buildings show that we can predict HL and CL with low mean absolute error deviations from the ground truth which is established using Ecotect (0.51 and 1.42, respectively). The results of this study support the feasibility of using machine learning tools to estimate building parameters as a convenient and accurate approach, as long as the requested query bears resemblance to the data actually used to train the mathematical model in the first place.