Estimation Of Building Energy Consumption Research Articles

Residential building energy consumption estimation: A novel ensemble and hybrid machine learning approach

In recent decades, there has been a substantial rise in both worldwide energy consumption and the accompanying increase in Carbon Dioxide (CO2) emissions, primarily propelled by population growth and the escalating demand for personal comfort. Operational energy consumption in buildings constitutes about 30% of the world's total final energy usage, underscoring the significance of predicting building energy usage for effective energy planning, management, and conservation. This study has enhanced the prediction of heating load (HL) and cooling loads (CL) for residential buildings using novel and dependable machine learning (ML) techniques. The study utilized two base models: Adaptive Boosting (ADA) and Extreme Gradient Boosting (XGBoost). An ensemble consisting of ADA and XGB was constructed to improve the model's performance, aligning with the principles of the Dempster-Shafer theory. To optimize the efficiency of ADA and XGB, five innovative optimizers, namely Victoria Amazonica Optimization (VAO), Giant Trevally Optimizer (GTO), Covariance Matrix Adaptation Evolution Strategy (CMAES), Coyote Optimization Algorithm (COA), and Mountain Gazelle Optimizer (MGO), were integrated. Statistical analysis has been employed to evaluate the performance of the proposed models. The results highlight the effectiveness of CMAES in optimizing the XGB and ADA models for predicting HL and CL. The most accurate result was achieved by the XGCM hybrid model, as evidenced by the impressive total R2 value of 0.9934 in HL and 0.9911 in CL prediction. The experimental findings illustrate that the suggested approach exhibits superior predictive performance across various scenarios of building energy consumption.

Preparation of high-strength waste-derived eco-friendly ceramic foam as face brick and its estimation of building energy consumption for thermal insulation

Waste-derived ceramic foams as a logical solution for the reutilization of industrial solid wastes have been prepared using red mud and fly ash, eggshell as a foaming agent, and di-sodium tetraborate as a glass former. The effect of foaming (1–5 wt %) and glass-forming (20 wt %) agents on the microstructure, physico-mechanical properties, and chemical stability of foams are investigated. The optimized product (RFE3) with a total porosity (33%) and bulk density (2.14 g/cc) reveals high compressive strength (109.75 MPa) and low thermal conductivity (0.93 W/m•K) that is suitable as a load bearing outside building block face brick. The theoretical building energy simulation model using heat balance (HBM) and cooling load temperature difference (CLTD) method has revealed that the average annual energy conservation in India is 21816 MJ. Compared with the traditional face brick, our developed high strength ceramic foam face brick with low thermal conductivity saves satisfactory building energy consumption. The present work is relevant to international research regarding environment friendly building envelope construction.

Effect of different generated weather climate data file on building energy consumption in different climates of Iran

Accurate estimation of building energy consumption is critical, and it can usually be calculated using simulation software like Energy Plus. The input weather data for software is typically a typical or reference year as a standard weather file, such as TMY2. To best estimate building energy consumption, TMY2 data must be generated intelligently. In the present study, first, several TMY2 data files have been generated from different periods (5–21 years) of available measured weather data (9 separate files). Available measured weather data is for 1998 to 2019. Also, a TMY2 file has been generated from the long-term average for the total period (1998–2019), and two TMY2 files have been generated using the CCWeatherGen software for 2020 and 2050. In the next step, the calculated building energy consumption from those files for an educational building will be compared using Energy Plus software. The primary purpose of this study is to investigate the impact of different generated TMY2 (from different weather data periods) on energy consumption and determine the best period to generate TMY2 files from the available period of 5 to 21 years for the capital of Iran (Tehran), which has a semi-arid climate, and Tabriz, which has a cold and mountainous climate. These cities have different climates. Results show that for Tehran, building heating energy consumption changes by about 10% from other generated TMY2 files (one file generated from 2009 to 2019 and another generated from the 2007 to 2019 period). The result shows that generating TMY2 files from different weather data periods affects building energy consumption. So, a suitable strategy for generating TMY2 files is necessary. As a new strategy, the present study proposes that TMY2 files should be generated based on building cooling and heating energy from recent years. Also, because climate change is an important parameter, a TMY2 file should be generated from recent years’ weather data to accurately predict building energy consumption.

High-resolution estimation of building energy consumption at the city level

High-resolution estimation of building energy consumption at the city level

An Automated Space-Based Graph Generation Framework for Building Energy Consumption Estimation

The 3D information in Building Information Modeling (BIM) has received significant interest for smart city applications. Recently, employing Industry Foundation Classes (IFC) for BIM in data-driven methods for Building Energy Consumption Estimation (BECE) has gained momentum because of the enriched geometric and semantic information. However, despite extensive studies on applying the IFC data in BECE analysis, employing the full potential of the BIM remains poor due to its complex data model and incompatibility with data-driven algorithms. This paper proposes a framework to extract accurate semantic, geometry, and topology information from the room-level (space) IFC schema by introducing new geo-computation algorithms to deal with these challenges. Additionally, we define a new topological weighted relationship between spaces in different stories by combining common geometry area with energy resistance value. Eventually, the proposed weighted space-based graph will be constructed to decrease the original complexity of the IFC model, and it is compatible with graph-based machine learning algorithms. The results are promising, with more than 90% accuracy in extracting the geometry information for the convex and non-convex polyhedron rooms and 100% accuracy in detecting vertical and horizontal adjacent rooms. This study confirms the proposed approach’s efficiency, accuracy, and feasibility for space-based BECE analysis.

Data-driven estimation of building energy consumption and GHG emissions using explainable artificial intelligence

Energy consumption prediction is an integral part of planning and controlling energy used in the building sector which accounts for 40% of the global energy consumption and a significant portion of greenhouse gas emissions. However, very few studies focused on the combined effect of building characteristics, building geometry, and urban morphology on energy performance. Such a research gap is addressed in this study by developing an explainable deep learning model. Our model uses Light Gradient Boosting Machine integrated with the SHapley Additive exPlanation algorithm, so as to provide insights into the feasibility of using machine learning-based models for energy performance prediction of buildings. With the proposed eXplainable Artificial Intelligence model, this study successfully predicts energy usage and greenhouse gas emissions of residential buildings, as well as identifies the most influential variables and evaluates their relative importance. A case study based on Seattle's data is used to verify the proposed framework, and some conclusions can be drawn: (1) Urban morphology and building geometry have significant effects on evaluating the building energy consumption and greenhouse gas emissions, as the accuracy of predicted result improve 33.46% compared with only considering building characteristics; (2) The total gross floor area and natural gas are identified as the most influential factors for energy consumption and GHG emissions, respectively; (3) The proposed model is examined to be an accurate method with the R2 of 0.8435 on average, comparing with the other approaches, such as the eXtreme Gradient Boosting, Random Forest, and Support Vector Regression. The main contributions of this research lie in that (a) a comprehensive structure integrated with building characteristics, building geometry, and urban morphology is established to forecast the energy use and greenhouse gas emissions; (b) an explainable artificial intelligence model incorporated with the SHapley Additive exPlanation algorithm into Light Gradient Boosting Machine has been proved to achieve an accurate prediction of the energy performance of residential buildings.

ROOM-BASED ENERGY DEMAND CLASSIFICATION OF BIM DATA USING GRAPH SUPERVISED LEARNING

Abstract. Nowadays, cities and buildings are increasingly interconnected with new modern data models like the 3D city model and Building Information Modelling (BIM) for urban management. In the past decades, BIM appears to have been primarily used for visualization. However, BIM has been recently used for a wide range of applications, especially in Building Energy Consumption Estimation (BECE). Despite extensive research, BIM is less used in BECE data-driven approaches due to its complexity in the data model and incompatibility with machine learning algorithms. Therefore, this paper highlights the potential opportunity to apply graph-based learning algorithms (e.g., GraphSAGE) using the enriched semantic, geometry, and room topology information extracted from BIM data. The preliminary results are demonstrated a promising avenue for BECE analysis in both pre-construction step (design) and post-construction step like retrofitting processes.

Season-Based Occupancy Prediction in Residential Buildings Using Machine Learning Models

• Data mining-based framework to predict occupancy accounting variation corresponding to different seasons is proposed. • Prediction performances of six machine learning algorithms are studied in terms of accuracy and computational time. • The recursive feature elimination with cross-validation technique is used for selecting optimal inputs. • The developed framework is generic and can be extended to a wide range of residential buildings. A reliable occupancy prediction model plays a critical role in improving the performance of energy simulation and occupant-centric building operations. In general, occupancy and occupant activities differ by season, and it is important to account for the dynamic nature of occupancy in simulations and to propose energy-efficient strategies. The present work aims to develop a data mining-based framework, including feature selection and the establishment of seasonal-customized occupancy prediction (SCOP) models to predict the occupancy in buildings considering different seasons. In the proposed framework, the recursive feature elimination with cross-validation (RFECV) feature selection was first implemented to select the optimal variables concerning the highest prediction accuracy. Later, six machine learning (ML) algorithms were considered to establish four SCOP models to predict occupancy presence, and their prediction performances were compared in terms of prediction accuracy and computational cost. To evaluate the effectiveness of the developed data mining framework, it was applied to an apartment in Lyon, France. The results show that the RFECV process reduced the computational time while improving the ML models’ prediction performances. Additionally, the SCOP models could achieve higher prediction accuracy than the conventional prediction model measured by performance evaluation metrics of F-1 score and area under the curve. Among the considered ML models, the gradient-boosting decision tree, random forest, and artificial neural network showed better performances, achieving more than 85% accuracy in Summer, Fall, and Winter, and over 80% in Spring. The essence of the framework is valuable for developing strategies for building energy consumption estimation and higher-resolution occupancy level prediction, which are easily influenced by seasons.

Impact of meteorological factors on high-rise office building energy consumption in Hong Kong: From a spatiotemporal perspective

This article aims to comprehensively analyse the impact of meteorological factors on the energy consumption of high-rise office buildings in Hong Kong. Fifty-seven runs of EnergyPlus simulations based on 30 years of actual hourly meteorological data between 1989 and 2018 measured from one urban and one rural site as well as the Typical Meteorological Year (TMY) were conducted to examine the joint energy impact of climate change and urban heat island (UHI) effect. Spatiotemporal inaccuracies of TMY-based simulations were discussed in error analysis by comparing with those using actual meteorological data. In longitudinal and cross-sectional analyses, significant energy impact of both climate change and UHI was found and quantitatively reported in terms of total and heating, ventilation and air conditioning (HVAC) energy consumption; long-term dynamics and the winter-dominant intra-annual distribution of the UHI-driven energy discrepancies were revealed; and the inconsistency between UHI-driven energy discrepancies and temperature discrepancies was found and explained. Regression analysis shows that for high-rise office buildings in Hong Kong, energy consumption is more sensitive to temperature and moisture change in hot and humid conditions than in cold and dry conditions; and air pressure may also serve as an indicator for a rough estimation of building energy consumption.

Data-driven estimation of building energy consumption with multi-source heterogeneous data

For better energy evaluation and management, a categorical boosting (CatBoost)-based predictive method is presented to accurately estimate building energy consumption by learning large volumes of multi-source heterogeneous data collected from buildings. To be specific, the newly-developed CatBoost model belonging to the ensemble learning has superiority in handling categorical variables and producing reliable results. As a case study, our proposed method is validated in a multi-dimensional dataset about Seattle's building energy performance provided by the city’s government, aiming to estimate the weather normalized site energy use intensity of buildings and characterize its non-linear relationship with other 12 possible influential features. Results from the 5-fold cross-validation demonstrate that the model exhibits a strong ability in predicting the exact value of energy intensity precisely, which can even outperform popular machine learning algorithms including random forest and gradient boosting decision tree under R2 of 0.897. Based on a defined threshold, these predicted values can be classified as the normal or abnormal energy consumption reaching an accuracy of 99.32% for outlier detection, which is helpful in alarming potential risks at an early stage and developing strategies to enhance the energy efficiency. Moreover, results from the established model can be interpreted objectively, suggesting that features concerning the physical and energy characteristics contribute more to energy estimation than environmental features. Since such results understand the building energy consumption and efficiency in a data-driven manner, they can eventually serve as guidance for building owners and designers in designing and renovating buildings to achieve better energy-conserving performance.

A Study on the Methodology of Building Energy Consumption Estimation and Energy Independence Rate for Zero Energy City Planning Phase

A Study on the Methodology of Building Energy Consumption Estimation and Energy Independence Rate for Zero Energy City Planning Phase

Prediction Model Based on an Artificial Neural Network for User-Based Building Energy Consumption in South Korea

The evaluation of building energy consumption is heavily based on building characteristics and thus often deviates from the true consumption. Consequently, user-based estimation of building energy consumption is necessary because the actual consumption is greatly affected by user characteristics and activities. This work aims to examine the variation in energy consumption as a function of user activities within the same building, and to employ an artificial neural network (ANN) to predict user-based energy consumption. The study exploited the actual 24-h schedules of 5240 single-person households and computed the respective energy consumption using EnergyPlus V 8.8.0 software. The calculated values were clustered according to gender, age, occupation, income, educational level, and occupancy period and the difference among them was analyzed. The simulation results showed that for single-person households in Korea, females used more energy than males did, and the difference increased with age. Furthermore, unemployed and low-income individuals consumed more energy whereas consumption was inversely proportional to the educational level. Energy consumption increased with the occupancy period. Based on the simulation results and six user characteristics, the ANN model indicated a correlation between user characteristics and energy usage. This study analyzed the differences in energy usage depending on user activity and characteristics that affect building energy consumption.

Estimation of building energy consumption using weather information derived from photovoltaic power plants

Abstract Photovoltaic power must be measured for billing purposes and to provide power injection information. To emphasise the importance of weather information derived from photovoltaic power data, we consider how building energy consumption is estimated. Photovoltaic power can be treated as an input to an energy consumption model rather than weather information (solar insolation, temperature, and/or relative humidity). We use a partial, mutual information algorithm for selection of the input variables required by a building consumption model; the data are derived from adjacent photovoltaic power stations. When weather information imparted by photovoltaic power is inadequate, the accuracy of energy consumption estimations can be improved by combining an empirical mode decomposition algorithm and an extreme-learning machine algorithm. Our energy consumption estimations, based on partial mutual information, empirical mode decomposition, and use of an extreme-learning machine, were verified using real data from Beijing and Guangzhou, China. The simulations show that the precision of estimation can be increased by fully exploiting the interdependence of photovoltaic power and building energy consumption.

Estimation and sensitivity analysis of building energy demand

In modern societies buildings largely contribute to the energy demand and account in some countries for up to 45% of the primary energy consumption. Detailed consumption data can be used to offer advanced services and provide new business opportunities to all participants in the energy supply chain. In recent years, the intensified research effort by scientific communities and companies from the energy sector has led to an improved estimation of building energy consumption. This paper proposes an innovative tool that combines the estimation of the building energy consumption and supply costs with a sensitivity analysis. The estimation procedure considers both the building design and user interactions with the building. The effect of small variations in the building configuration on the building energy demand and supply costs is determined by the sensitivity analysis. The identification of critical parameters allows building managers to adopt appropriate measures to improve the energy efficiency of the considered building. For the demonstration and validation of the proposed approach, the tool is applied to a case of an office building located in Madrid (Spain). It is explained in detail how the estimates of the building energy consumption and the costs were calculated and the sensitivity analysis performed.

On the development of multi-linear regression analysis to assess energy consumption in the early stages of building design

Modeling of energy consumption in buildings is essential for different applications such as building energy management and establishing baselines. This makes building energy consumption estimation as a key tool to reduce energy consumption and emissions. Energy performance of building is complex, since it depends on several parameters related to the building characteristics, equipment and systems, weather, occupants, and sociological influences. This paper presents a new model to predict and quantify energy consumption in commercial buildings in the early stages of building design. Building simulation software including eQUEST and DOE-2 was used to build and simulate individual building configuration that were generated using Monte Carlo simulation techniques. Ten thousands simulations for seven building shapes were performed to create a comprehensive dataset covering the full ranges of design parameters. The present study considered building materials, their thickness, building shape, and occupant schedule as design variables since building energy performance is sensitive to these variables. Then, the results of the energy simulations were implemented into a set of regression equation to predict the energy consumption in each design scenario. A good agreement was seen between the predicted data based on the developed regression model and DOE simulation and the maximum error was less than 5%. It is envisioned that the developed regression models can be used to estimate the total energy consumption in the early stages of the design when different building schemes and design concepts are being considered.

A review on the basics of building energy estimation

Abstract Energy security, environmental concerns, thermal comfort, and economic matters are driving factors for the development of research on reducing energy consumption and the associated greenhouse gas emissions in every sector of the economy. Building energy consumption estimation has become a key approach to achieve the goals on energy consumption and emissions reduction. Energy performance of building is complicated since it depends on multiple variables associated to the building characteristics, equipment and systems, weather, occupants, and sociological influences. This paper aims to provide an up-to-date review on the basics of building energy estimation. Regarding models, a classification for energy estimation models is proposed based on the different classifications found in the literature review. The paper focuses on models developed with whole building energy simulation software and their validation. This focus is justified because of the importance that whole building energy tools have gained on areas such as green building design, and analysis of energy conservation strategies and retrofits. Since a suitable weather file is a major component for reliably simulations, the section about weather data provides pertinent information.