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The heat transfer coefficient (HTC) is a very important factor influencing the energy performance of a building. Recent studies have shown the importance of on-site measurements of the HTC in reducing the performance gap in buildings. However, its measurement setup and calculation procedures are known to be intense and complex. Due to this, many stakeholders in the building industry find it impractical and insufficient for their needs. This paper presents the results of an international survey that targets such stakeholders with the aim to get their perspectives on HTC measurements on-site. Several stakeholders from 14 countries in Europe participated in the survey. The survey is categorized into four parts: a) basic data about the participants, b) their interest in methods for measured energy performance, c) their views on the characteristics of such a methodology and d) their concerns and opportunities. The results reveal that the stakeholders are highly interested in measuring the HTC on-site. The results also provide interesting insights on the aspects relevant for them and their customers. In particular, we elaborate on their perspective on the time to conduct the measurement, the cost of the setup, the measurement duration and the acceptable error. The assimilated understanding from the survey will help the building and the construction industry to identify opportunities for a progressive assessment campaign involving on-site measurements. This study is part of the International Energy Agency's Energy in Buildings and Communities Programme (IEA EBC) Annex-71 project titled ‘Building energy performance assessment based on optimized in-situ measurements’., Renewable and Sustainable Energy Reviews, 144, ISSN:1364-0321

Building-related heating consumes 80% of final energy in the Swiss residential sector. Electrical resistance heating systems consumed 9.3 PJ and 7.9 PJ for space and domestic hot water heating respectively. This is 8.6% of the total electricity consumption in Switzerland. A successful retrofit action requires knowledge of energy consumed by these systems in the pre-retrofit stage. However, it is difficult to deduce the heating energy from the overall electrical load profile of each residence. One of the primary reasons is irregularities in the operation of the heating system. We address this by applying a data mining approach that group data points based on their concentration in the time axis. This is done by using DBSCAN (Density-based spatial clustering of applications with noise) which requires derivation of two clustering parameters, ‘Epsilon’ and ‘MinPts’. We present a method to automatically derive these two parameters based on a combination of histogram analysis and range search method. We also compare our results with those obtained by k-means and density peaks clustering methods. The proposed methodology can effectively disaggregate electrical load into space and domestic hot water heating for residences with an electrical resistance heating system.

In order to meet the ambitious emission-reduction targets of the Paris Agreement, energy efficient transition of the building sector requires building retrofit methodologies as a critical part of a greenhouse-gas (GHG) emissions mitigation plan, since in 2050 a high proportion of the current global building stock will still be in use. This paper reviews current retrofit methodologies with a focus on the contrast between data-driven approaches that utilize measured building data, acquired through either 1) on-site sensor deployment or 2) from pre-aggregated national repositories of building data. Differentiating between 1) bottom-up approaches that can be divided into white-, grey- and black-box modelling, and 2) top-down approaches that utilize analytical methods of clustering and regression, this paper presents the state-of-the-art in current building retrofit methodologies; outlines their strengths and weaknesses; briefly highlights the challenges in their implementation and concludes by identifying a hybrid approach - of lean in-situ measurements supplemented by modelling for verification - as a potential strategy to develop and implement more robust retrofit methodologies for the building stock., Renewable and Sustainable Energy Reviews, 144, ISSN:1364-0321

Applied Energy, 294, ISSN:0306-2619, ISSN:1872-9118

This study aims to develop prediction models for HVAC related energy saving in office buildings. The data-driven modelling makes use of data gathered from several energy audit reports. These reports entail building and energy consumption data for 56 office buildings in Singapore. The two models are developed using Multiple Linear Regression (MLR) and Artificial Neural Network (ANN). The methodology to select the most appropriate input variables forms the essence of this study. This variable selection procedure involves 819,150 iterations, taking all possible combinations of the 14 input variables to determine the most accurate model. The dependent variable is taken as the change in energy use intensity (EUI, measured in kWh/m2.year) between pre- and post-retrofit conditions. The results show that the ANN model is more accurate with a mean absolute percentage error (MAPE) of 14.8%. The best combination of variables to achieve this comprises of gross floor area (GFA), air-conditioning energy consumption, operational hours and chiller plant efficiency. The information on these four variables, along with the prediction model can be used to predict HVAC related energy savings in office buildings to be retrofitted.

This study aims to determine key building variables influencing energy consumption in air-conditioned office buildings. The study is based in Singapore which entails tropical climatic conditions. The analysis is based on assessment of several energy audit reports concerning pre- and post-retrofit data from 56 office buildings. A list of 14 building variables, extracted from these reports form the superset. These are systematically analyzed further to derive key variables influencing energy consumption and retrofitting decisions. For this purpose, a robust iterative process is developed utilizing k-means clustering. This process tests all combinations of the 14 variables against change in energy use intensity (EUI, measured as kWh/m2.year) for pre- and post-retrofit conditions. The results indicate that the best set of variables consists of: 1) gross floor area (GFA), 2) non-air-conditioning energy consumption, 3) average chiller plant efficiency, and 4) installed capacity of chillers. This information can be utilized to explore energy saving potential of office buildings that need to be retrofitted. The resultant clusters can also be used to benchmark buildings based on pre-retrofit conditions and energy saving potential.

Occupancy information is one of the crucial variables in modelling and predicting the energy use in buildings. However, the presence of occupants is often stochastic in nature. In the presented case of eight space typologies derived from three institutional building blocks, a substantial variation in the correlation between occupancy and energy consumption is found for different space types during the semester and semester breaks for various resolutions of day and time. Further, it has been identified that a weak correlation between occupants and energy use is due to the use of common plug and lighting loads such as office printers, projectors, lab instruments, and fluorescent lamps. However, in the spaces studied, such as offices and computer rooms, the control of plug and lighting loads can be at individual occupancy levels for avoiding energy wastages. To characterize occupancy and energy consumption patterns by different space types and occupant types, this study develops and presents an integrated, data-driven modelling framework and results for different space types (like classrooms, studios, computer rooms, office spaces and laboratories and time resolution (hourly to semester-long intervals) for the case study building. It is found that the Deep Neural Network (DNN) model exhibits a slightly better prediction accuracy than conventional machine learning/regression models such as gradient boosting, support-vector network, and feed-forward neural network. However, in terms of computation time, the gradient boosting model is found to be faster than the DNN model for comparable outcomes. The developed integrated model will better equip the building facility management for an occupant-oriented control of building systems for energy efficiency.

Energy consumption forecasting for buildings has immense value in energy efficiency and sustainability research. Accurate energy forecasting models have numerous implications in planning and energy optimization of buildings and campuses. For new buildings, where past recorded data is unavailable, computer simulation methods are used for energy analysis and forecasting future scenarios. However, for existing buildings with historically recorded time series energy data, statistical and machine learning techniques have proved to be more accurate and quick. This study presents a comprehensive review of the existing machine learning techniques for forecasting time series energy consumption. Although the emphasis is given to a single time series data analysis, the review is not just limited to it since energy data is often co-analyzed with other time series variables like outdoor weather and indoor environmental conditions. The nine most popular forecasting techniques that are based on the machine learning platform are analyzed. An in-depth review and analysis of the ‘hybrid model’, that combines two or more forecasting techniques is also presented. The various combinations of the hybrid model are found to be the most effective in time series energy forecasting for building.

Clustering algorithms have been successfully applied in analyzing building energy consumption data. It has proven to be an effective technique to identify representative energy consumption patterns as well as being a pre-processing step for other techniques. In this paper, we propose a clustering method based on k-shape algorithm, which is a relatively novel method to identify shape patterns in time-series data. In the experiment, clustering is performed for each individual building according to its hourly consumption. The novelty of this paper is that a new k-shape algorithm is applied to detect building-energy usage patterns at different levels, and the clustering result is further utilized to improve the accuracy of forecasting models. Ten institutional buildings covering three different typologies are used as case studies and a set of hourly and weekly energy consumption data is further analyzed in this paper. The experimental results reveal that this proposed method can detect building energy usage patterns in different time granularity effectively and also proves that the forecasting accuracy of SVR model is significantly improved by utilizing the results of the proposed clustering method.

Thermal comfort aspects of indoor spaces are crucial during the design stages of building layout planning. This study presents a simplified tool based on thermal comfort using predicted mean vote (PMV) index. Thermal comfort simulations were performed for 14 different possible room layouts based on window configurations. ECOTECT 12 was used to determine the PMV of these rooms for one full year, leading to 17,808 simulations. Simulations were performed for three different climatic zones in India and were validated using in-situ measurements from one of these climatic zones. For moderate climates, rooms with window openings on the south facade exhibited the best thermal comfort conditions for nights, with comfort conditions prevailing for approximately 79.25% of the time annually. For operation during the day, windows on the north facade are favored, with thermal comfort conditions prevailing for approximately 77.74% of the time annually. Similar results for day and night time operation for other two climatic zones are presented. Such an output is essential in deciding the layout of buildings on the basis of functionality of the different rooms (living room, bedroom, kitchen) corresponding to different operation times of the day.

The current retrofit rate in Switzerland is very low. Among other reasons this is attributed to a lack of knowledge about cost-optimal retrofit strategies. In this work we carry out a holistic cost-optimal retrofit analysis for a Swiss single-family house. First, currently popular retrofit measures including all incurring lifecycle costs are identified and collected. Thereby, building systems as well as building envelope retrofit measures are considered. The retrofit measures are then combined to form a wide range of applicable retrofit strategies which are assessed in terms of cost-effectiveness and environmental impact. The calculation methodology considers the impact of building envelope retrofit measures on the design and efficiency of the heating system. For the case study building, the most cost-optimal retrofit strategy is to combine a geothermal heat pump with inter-rafter roof insulation and cellar ceiling insulation. The results indicate that the choice of heating system is much more crucial in terms of cost-effectiveness than building envelope measures. With respect to reducing primary energy consumption, building envelope measures are indispensable, however, when aiming for reducing greenhouse gas emissions, the choice of heating system becomes dominant., Journal of Physics: Conference Series, 1343 (1), ISSN:1742-6588, ISSN:1742-6596

Electricity load forecasting is crucial for effective operation and management of buildings. Support Vector Regression (SVR) have been successfully used in solving nonlinear regression and time series problems related to building energy consumption forecasting. As the performance of SVR heavily depends on the selection of its parameters, differential evolution (DE) algorithm is employed in this study to solve this problem. The forecasting model is developed using weighted SVR models with nu-SVR and epsilon-SVR. The DE algorithm is again used to determine the weights corresponding to each model. A case of time series energy consumption data from an institutional building in Singapore is used to elucidate the performance of the proposed model. The proposed model can be used to forecast both, half-hourly and daily electricity consumption time series data for the same building. The results show that for half-hourly data, the model exhibits higher weight for nu-SVR, whereas for daily data, a higher weight for epsilon-SVR is observed. The mean absolute percentage error (MAPE) for daily energy consumption data is 5.843 and that for half-hourly energy consumption is 3.767 respectively. A detailed comparison with other evolutionary algorithms show that the proposed model yields higher accuracy for building energy consumption forecasting.

This paper presents a detailed investigation and analysis of the energy consumption characteristics of three institutional buildings in Singapore. Building information, energy consumption data of the air-conditioning system, and energy consumption data of the plug loads were collected separately. Identification models are developed to predict the real daily energy consumption data. Developed models involve three specific functions to represent the variability of the daily occupancy, the additional occupancy due to visitors and the variation of outdoor air temperature. The performance of the developed identification model is very satisfactory and fits very well with the real energy consumption data. Based on the identification model, key factors which are influencing the energy consumption in institutional buildings are identified as the variability of the daily occupancy, hence a methodology is developed to calculate the occupancy in the building. The identified parameters are used as inputs into deterministic energy simulation programs, like Energy Plus, to perform detailed energy analysis. The whole methodology developed has improved significantly the accuracy of the energy simulation modeling of institutional buildings and has permitted to understand and evaluate the major energy characteristics of these buildings.

This study presents a methodology to forecast diurnal cooling load energy consumption for institutional buildings using data driven techniques. The cases for three institutional buildings are examined. A detailed analysis on their energy consumption data for two years shows that there is a high variation in diurnal energy consumption. This is largely attributed to the university scheduling and vacation periods. To reduce the degree of variation, the energy consumption data is divided into classes. These class numbers are then taken as inputs for the forecasting model which is developed using Artificial Neural Networks (ANN). The results show that the ANN is able to train and forecast the next day energy use based on five previous days’ data with good accuracy. The model development, along with ANN architecture used in this case is discussed in detail. As a next step, the forecasted output is taken back as an input with a view to forecast the output of the following day. This step is repeated and the model exhibits an R2 of more than 0.94 in forecasting the energy consumption for the next 20 days. It is also noted that such a methodology can be positively extended to other institutional buildings.

The thermal performance gap in buildings is defined as the difference between the theoretical and the actual energy consumption for heating, and is known to undermine energy retrofit strategies and policies. This study examines the performance gap in retrofitted buildings using the Swiss Cantonal Energy Certificate for Buildings (CECB) database, using a sample of 1172 buildings for which both theoretical and actual metered consumption were known. We found an average negative performance gap of –23% for pre-retrofit buildings (actual consumption smaller than calculated) and instead a good approximation of actual consumption with theoretical consumption after retrofitting (a positive gap of 2%). A regression analysis on the energy performance certificate input parameters characterizing the building led to the conclusion that these are poor predictors of actual consumption compared to the theoretical calculation: parameters such as the energy label and the thermal proprieties of the envelope (U-values) have minor explanatory power for the actual consumption despite explaining a high degree of change in the theoretical consumption. Analysis of the indicator Energy Savings Deficit (ESD) shows an overestimation (of 37%) of the achievable savings on the basis of the theoretical consumption, whereas the prediction of savings using measured consumption before retrofit resulted in a good agreement with the actual savings (3.6% overestimation). This implies that energy savings can be estimated rather accurately by comparing the actual current consumption with the expected theoretical consumption defined by the certificate after retrofit., Energy and Buildings, 224, ISSN:0378-7788, ISSN:1872-6178

A high-quality building energy retrofit analysis requires knowledge of building characteristics like the type of installed heating system. This means auditing the building in person or conducting a detailed survey, which is not readily scalable for many buildings. This paper presents a data-driven methodology to identify building characteristics from raw smart meter data sets to allow large scale, high-quality building retrofit analysis. We use the concept of energy signatures, a scatter plot with outside air temperature on the x-axis and electricity consumption on the y-axis, which condenses each building’s electricity use into one highly informative graph. Using a Support-Vector Regression model we extract the shape of each signature and cluster them subsequently. Dynamic time warping is used to align the signature shapes of all buildings. In two case studies, consisting of smart meter data sets from 408 and 480 buildings respectively, we show that our clusters correlated well to the heating system type and the building type by comparing to building-level metadata or demographic data.

Accurate building energy assessments are often limited by inaccurate assumptions about the buildings' properties and occupant behavior. Many assessment methods use assumptions instead of measured data because it is cumbersome, time-intensive, and cost-intensive to acquire up-to-date, on-site measured data. This work bridges this gap by presenting the design of an open-source, wireless sensor network (WSN) that is easy to program, manufacture, modify, and deploy. The version of the WSN presented here measures air temperature, relative humidity, supply and return temperature of hot water from the heating system and the radiators, heat flux through the walls and the windows, luminosity, oil flow, electricity, window opening times, and CO2-concentration. The modularity of the WSN allows it to be extended with the addition of new types of sensors to the existing hardware framework. The data is streamed to an online database, allowing monitoring of the measurements in near real-time. The cost and performance of the sensor network are intended to be as good as or better than the existing solutions. The current cost for one temperature sensor node starts at 116 USD. The observed battery lifetime reaches up to a year for a five-minute sampling interval. The average installation time for a sensor node is 7 min. We describe the hardware architecture, software architecture, cost of the WSN, along with results from co-location tests, and a deployment in an occupied building.

Buildings are dynamic thermal systems that require energy to maintain a comfortable indoor environment. Knowing these dynamics allows for identifying relevant building characteristics for assessing building energy performance. However, conventional building simulation programs take daily or monthly mean values for performance assessment. These often aggregate the errors and uncertainties between the theoretical and the actual building performance while missing the temporal dynamics on a minute or hourly basis. The objective of this study is to uncover the temporal aspects of building properties using data obtained by a wireless sensor network (WSN). Our analysis focuses on the following seven variables that are relevant for assessing building performance and retrofit measures: indoor air temperature, window opening instances, CO2 concentration, supply temperature from the heating system, outdoor air temperature, heat flux through the wall and the window. Using a single-family residence as a case study, we identify the temporal dynamics of these variables using polar plots on a high-resolution, 5-minute interval dataset. Following this, we define a set of conditional rules to study ‘expected’ and ‘unexpected’ impact of the six variables on the indoor air temperature. We observe strong temporal dynamics for certain building components, resulting in a large time lag. The conditional rule analysis also allows identifying energy saving potentials and factors contributing to the performance gap. For example, we observe high indoor air temperatures and at times when no occupants are present. Finally, we discuss the benefits of this approach with respect to building retrofit and energy performance gap analysis.

This study presents building energy forecasting methodology for cooling load for three institutional buildings. These buildings belong to a university campus in Singapore. The daily energy consumption for cooling load is obtained for a period of two years and the daily variation is analysed. The energy consumption is initially divided into five classes and the class numbers are used as inputs to develop a forecasting model. The model is developed using two machine learning tools. The tools used are Artificial Neural Network (ANN) and Adaptive Neuro Fuzzy Interface System (ANFIS). The division of data for training and testing the model is nearly 60% and 40% respectively. The results show that both ANN and ANFIS forecast the cooling load energy consumption of the three buildings with good accuracy. The correlation coefficient between measured and predicted consumption for training data are well above 0.98. The same is well above 0.96 for testing data. It is noted that such a methodology can be positively extended to other institutional buildings in the campus.

This study presents an investigation and discussion on the energy performance of three tertiary institutional buildings in Singapore. Building information, energy consumption data of the air-conditioning system, and energy consumption data of the plug loads were collected separately. MATLAB identification models are developed to simulate the real daily energy consumption data:. Three functions are introduced to represent the function of daily occupancy, function of additional occupancy due to visitors and the function of outdoor air temperature. The results show that the predicted value follows the trend of real energy consumption value very well and can predict the daily variations. The newly developed methodology is able to simulate the daily variations of energy consumption. R 2 of 0.54, 0.66 and 0.63 can be achieved for the three buildings respectively

Air pollution and climate change increased the importance of renewable energy resources like solar energy in the last decades. Rack-mounted PhotoVoltaics (PV) and Building Integrated PhotoVoltaics (BIPV) are the most common photovoltaic systems which convert incident solar radiation on facade or surrounding area to electricity. In this paper the performance of different solar cell types is evaluated for the tropical weather of Singapore. As a case study, on-site measured data of PV systems implemented in a zero energy building in Singapore, is analyzed. Different types of PV systems (silicon wafer and thin film) have been installed on rooftop, facade, car park shelter, railing and etc. The impact of different solar cell generations, arrays environmental conditions (no shading, dappled shading, full shading), orientation (South, North, East or West facing) and inclination (between PV module and horizontal direction) is investigated on performance of modules. In the second stage of research, the whole PV systems in the case study are simulated in EnergyPlus energy simulation software with several PV performance models including Simple, Equivalent one-diode and Sandia. The predicted results by different models are compared with measured data and the validated model is used to provide simulation-based energy yield predictions for wide ranges of scenarios. It has been concluded that orientation of low-slope rooftop PV has negligible impact on annual energy yield but in case of PV external sunshade, east facade and panel slope of 30–40° are the most suitable location and inclination.

An attempt is made to present a new scale to study urban microclimates and outdoor thermal comfort using simple in-situ measurement data. For this purpose, six urban locations with distinct physical characteristics are selected in a metropolitan city, Chennai. At each location, three streets with diverse orientations (North-south; East-west and Northeast-southwest) are identified and their microclimatic conditions are monitored during the summer months of April, May and June. The variations in microclimate are studied using ANOVA single factor test and later, correlated with the site's physical characteristics. The assessment of microclimate and outdoor thermal comfort is done using Physiological equivalent temperature (PET).The results show that the site physical factors like the H/W ratio and green cover index show poor and moderate correlations respectively (R2 of 0.016 and 0.445) when regressed with the mean PET. In this regard the proposed HXG (read as H cross G) scale displays satisfactory correlation (R2 = 0.648). The HXG scale constitutes a product between the height to width (H/W) ratio and green cover percentage of a location and provides a fair idea about the locality's microclimatic conditions. This method to study and classify urban microclimates may prove useful in the context of urban design and planning. � 2011 Elsevier Ltd.

The recent Indian Railway budget proposes upgrading and development of fifty railway stations to world class standards. These stations act as crucial transport nodes for effective operation of the railway network and passenger well being. One of the important aspects regarding passenger satisfaction in these places is an acceptable thermal environment. This article studies the thermal comfort of passengers in a large and significant railway station in South India in the summer month of June. The study entails field measurements and questionnaire responses from 402 individuals over a period of fifteen days. The thermal comfort is estimated using the air temperature and the PET (Physiological Equivalent Temperature) scale. The neutral temperature obtained through questionnaire surveys is 31.93�C. In presence of fair air movement, there is some relaxation to neutral temperature, although the range of relaxation is much narrower than the models presented by other researchers. Comparing the Thermal sensation votes (ASHRAE 7-point scale) with comfort votes (Bedford 7-point scale), it is observed that the passengers exhibit high tolerance and adaptivity. Further, some observations are made on the relationship between the nature of waiting areas and their spatial influence on passenger thermal comfort. � 2010 Elsevier Ltd.

ispartof: EBC Annex 71: Building Energy Performance Assessment Based on In-situ Measurements - Challenges and General Framework status: Published online