Current building energy benchmarking relies on annual Energy Use Intensity (EUI), which cannot detect temporal operational anomalies—such as after-hours equipment operation or irregular scheduling—that represent actionable efficiency oppo...
With the development of metering technologies, data mining techniques such as machine learning have been increasingly used for the prediction of building energy consumption. Among various machine learning methods, the KNN algorithm was implemented to...
Forest above-ground biomass (AGB) can be estimated based on light detection and ranging (LiDAR) point clouds. This paper introduces an accurate and detailed quantitative structure model (AdQSM), which can estimate the AGB of large tropical trees. AdQ...
Forecasting the electricity consumption is an essential activity to keep the grid stable and avoid problems in the devices connected to the grid. Equaling consumption to electricity production is crucial in the electricity market. The grids worldwide...
The industrial sector accounts for a significant proportion of total energy consumption. Factory Energy Management Systems (FEMSs) can be a measure to reduce energy consumption in the industrial sector. Therefore, machine learning (ML)-based electric...
The estimation of pasture productivity is of great interest for the management of animal grazing. The standard method of assessing pasture mass requires great effort and expense to collect enough samples to accurately represent a pasture. This work p...
One of the most important research topics in smart grid technology is load forecasting, because accuracy of load forecasting highly influences reliability of the smart grid systems. In the past, load forecasting was obtained by traditional analysis t...
This paper presents an application of the Simulink stvmgain S-function for the thermal modelling of a building zone based on the resistance–capacitance scheme of EN ISO 13790. That model in the form of the state-space matrix with time-varying e...
Massive data can be collected from meters to analyze the energy use behavior and detect the operation problems of buildings. However, missing and abnormal data often occur for the raw data. Effective data filling and smoothing methods are required to...
Building load prediction is one of the important means of saving energy and reducing emissions, and accurate cold load prediction is conducive to the realization of online monitoring and the optimal control of building air conditioning systems. There...
Reductions in energy consumption and greenhouse gas emissions are required globally. Under this background, the Multilayer Perceptron machine-learning algorithm was used to predict liquid natural gas consumption to improve energy consumption efficien...
Eucalyptus plantation forests in southern China provide not only the economic value of producing timber, but also the ecological value service of absorbing carbon dioxide and releasing oxygen. Based on the theory of spatial colonial modeling, this pa...
The amount of heat lost through the envelope of a building is one of the most important variables that affects the energy performance evaluation of a house. In addition, it is especially important to estimate and accurately diagnose the amount of hea...
This study investigated the building energy retrofit potential of a shopping mall in Bangkok, Thailand, using a combined building energy modeling and economic analysis approach to achieve a balance between carbon emission reduction and financial feas...
Building energy simulation models have been used to assist the design and/or optimization of buildings energy performance. The results from building energy simulation models can be more reliable when measured energy use data, indoor environmental con...
Conventional building energy models (BEM) for heating and cooling energy-consumption prediction without calibration are not accurate, and the commonly used manual calibration method requires the high expertise of modelers. Bayesian calibration (BC) i...
This paper proposes the optimal algorithm for controlling the HVAC system in the target building. Previous studies have analyzed pre-selected algorithms without considering the unique data characteristics of the target building, such as location, cli...
This study outlines the preliminary stages of the development of an algorithm to predict the optimal WQ of the Hwanggujicheon Stream. In the first stages, we used the AdaBoost algorithm model to predict the state of WQ, using data from the open artif...
Short-term building energy consumption prediction is of great significance for the optimized operation of building energy management systems and energy conservation. Due to the high-dimensional nonlinear characteristics of building heat loads, tradit...
Predicting building energy consumption needs to be anticipated to save building energy and effectively control the predictions. This study depicted the target building as a physical model to improve the learning performance in a data-scarce environme...
In the process of collecting operational data for the performance analysis of water-cooled centrifugal chillers, missing values are inevitable due to various factors such as sensor errors, data transmission failures, and failure of the measurement sy...
This paper presents the results of a study that developed improved inverse models to accurately predict the annual daylighting performance (sDA and lighting energy use) of various window configurations. This inverse model is an improvement over previ...
Field measurements undertaken in six paired primary school classrooms in Anambra State, Nigeria, previously demonstrated that interlocking compressed earth block (ICEB) classrooms maintained significantly lower occupied-hour temperatures than adjacen...
The purpose of our work is to leverage the use of artificial intelligence for the emergence of smart greenhouses. Greenhouse agriculture is a sustainable solution for food crises and therefore data-based decision-support mechanisms are needed to opti...
This paper presents an energy performance assessment and the development of an energy consumption baseline model for a quarry located in Gauteng Province, South Africa. Using 24 months of historical electricity consumption and production data, the en...
There exists a notable research gap concerning the application of ice storage systems in shopping mall settings at the urban scale. The characteristics of large pedestrian flow, high energy consumption, and high peak loads in shopping malls make thei...
The need to reduce energy consumption in buildings is an urgent task. Increasing the use of calibrated building energy models (BEM) could accelerate this need. The calibration process of these models is a highly under-determined problem that normally...
Neural network models are data-driven and are effective for predicting and interpreting nonlinear or unexplainable physical phenomena. This study collected building information and heating energy consumption data from 16,158 old houses, selected key...
Accurate forecasting of a building thermal performance can help to optimize its energy consumption. In addition, obtaining the Heat Loss Coefficient (HLC) allows characterizing the thermal envelope of the building under conditions of use. The aim of...
In this study, a regression model was developed using the thermo-regulated residual refinement regression model (TRRM) analysis method based on three years and four months of in situ data collected from two water-cooled centrifugal chillers installed...
Renewable energy system (RES) is an environmentally friendly source of energy. A suitable design of RES is crucial to implement an energy-efficient building such as a zero energy building (ZEB). The significance of appropriate decision-making for the...
This study introduces a novel approach to building condition assessment (BCA) by combining traditional manual grading with machine learning models—artificial neural networks (ANNs) and random forests (RFs). Individual building components (e.g.,...
This study investigates a Proxy-Calibration method for modeling Variable Refrigerant Flow (VRF) systems in TRNSYS, addressing the absence of a dedicated simulation component. The approach approximates part-load behavior through indoor-unit combinatio...
In the present paper, the energy performance of buildings forming a school centre in the northwest of Spain was analyzed using a transient simulation of the energy model of the school, which was developed with TRNSYS, a software of proven reliability...
This study proposes a methodology for fairly assessing the building energy usage level of occupants using a public open dataset. A case study of day-care centers in South Korea was conducted to demonstrate the methodology. An open dataset of monthly...
This study was conducted to develop an artificial neural network (ANN)-based prediction model that can calculate the amount of cooling energy during the setback period of accommodation buildings. By comparing the amount of energy needed for diverse s...
The built environment has the potential to contribute to maintaining a reliable grid at the demand side by offering flexibility services to a future Smart Grid. In this study, an office building is used to demonstrate forecast-driven building energy...
Indoor CO2 concentration is considered a metric of indoor air quality that affects the health of occupants. In this study, a hybrid model was developed for forecasting the varying indoor CO2 concentration levels in a residential apartment unit in the...
Given the heterogeneity in data on heat pump performance curves across manufacturers, selecting the appropriate one for more detailed studies is complex. Machine learning (ML) techniques can be very helpful in this endeavor. In this case, four techni...
The Heat Loss Coefficient (HLC) characterizes the envelope efficiency of a building under in-use conditions, and it represents one of the main causes of the performance gap between the building design and its real operation. Accurate estimations of t...
The heating load of residential buildings is closely related to the local microclimate. However, there is a lack of quantitative indicators for assessing the impact of the outdoor microclimate on building heating loads in Lhasa residential buildings....
Accurate prediction of building indoor temperatures and thermal demand is of great help to control and optimize the energy performance of a building. However, building thermal inertia and lag lead to complex nonlinear systems is difficult to model. I...
The refrigeration cycle of an air source heat pump system is an important information that reveals critical operating data, such as the cooling capacity, power consumption, and performance of a system during operation. Operating data, such as refrige...
Building electricity load forecasting models are usually selected on an unchanged historical test set, although rankings may shift when temporal organization changes. We evaluated five models across 60 non-residential buildings using seven Fourier-su...
This study proposes a new method to accurately evaluate the overall building envelope thermal performance considering the window–wall correlation, providing a new tool for building thermal design. Firstly, a non-stationary room heat transfer mo...
Building energy signature analysis is a well-established tool for understanding the temperature sensitivity of building energy consumption and measuring energy savings. This tool has been used to measure energy savings of residential, commercial, and...
Accurate forecasting of cooling loads is essential for the effective operation of Building Energy Management Systems (BEMSs) and the reduction of building-sector carbon emissions. Although Artificial Neural Networks (ANNs), particularly Multi-Layer P...
Electrical load time series exhibit strong heterogeneity across daily patterns driven by calendar effects and behavioral variability, leading many forecasting models to favor dominant weekday profiles while degrading on weekends, holidays, and transi...
This study aimed to develop a control algorithm that can operate a variable refrigerant flow (VRF) cooling system with optimal set-points for the system variables. An artificial neural network (ANN) model, which was designed to predict the cooling en...
Humans spend approximately 90% of the daytime in buildings, and greenhouse gases (GHGs) emitted by buildings account for approximately 20% of total GHG emissions. As the energy consumed during building operation from a building life-cycle perspective...
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