U-value Of Wall Research Articles

Hybrid building energy modeling method with parameterized prototype models and rapid calibration

Building energy models were widely used in building energy performance analysis and efficiency improvement, such as building demand response assessment and energy system optimization. However, building energy modeling was a time-consuming and complex process. Therefore, this paper proposed a hybrid building energy modeling method based on parameterized prototype models and rapid calibration. The main feature of the method was the rapid generation of target building energy models using the prototype building energy performance database. The parameterized prototype building energy model considered 13 uncertainty parameters. Based on the range of 13 uncertainty parameters, such as wall U-value and solar heat gain coefficient, 1000 simulations were performed by Monte Carlo sampling to generate the prototype building energy performance database. The target building energy model was quickly calibrated by learning from this database. It filtered the models that met the requirements based on the actual building measurement data. Based on this hybrid modeling method, the rapid modeling tool AutoBPS-Hybrid was developed in a ruby environment. Shopping mall buildings located in three different climate zones were selected as case studies for this study. The results showed that if only one building energy model meeting the percentage errors was needed, only 3–4 simulations were required. If it was necessary to match the real uncertainty parameter distributions, an average of about 53 simulations was needed. The building energy models were applied to plug load and lighting control in buildings. The shopping mall buildings in Harbin, Beijing and Chengdu could reduce energy consumption by 10.18–13.33 kWh/m2, 14.43–18.15 kWh/m2 and 11.54–14.69 kWh/m2 per year, respectively.

Multi-objective optimization of building energy consumption and thermal comfort based on SVR-NSGA-II

It is necessary to reduce the huge energy consumption of buildings while ensuring indoor thermal comfort. The building envelope design notably affects the energy consumption and indoor thermal comfort. This study proposes a framework of support vector regression non-dominated sorting genetic algorithm-II (SVR-NSGA-II) to study the multi-objective optimization of buildings and discover the best building envelope design. First, the data are obtained by OpenStudio 1.1.0 simulation. Then SVR is used to describe the relationships between the parameters and the two objectives. Using SVR models as the objective functions, NSGA-II is applied to optimize the objectives. Finally, taking a proposed office building as an example, the developed framework is applied, verifying its feasibility and effectiveness. The optimized parameters of the building envelope are as follows: the exterior wall U-value, roof U-value, exterior wall U-value, solar heat gain coefficient value (SHGC), and south, north, east, west window-to-wall ratios are respectively equal to 0.664 , 0.393 , 1 , 0.381, 0.697, 0.7, 0.273, and 0.254. The design of the proposed building can be modified to provide a more energy efficient and comfortable building. The proposed model saves time and simplifies operation compared with traditional building information modeling methods.

Experimental and numerical study of a novel composite building wall U-value

The present study aims to minimize the overall heat transfer coefficient (U-value) and heat flux through building walls to improve indoor thermal comfort, using different bricks and insulating materials in building wall construction. The building walls of three different types of bricks (Type-1, Type-2, and Type-3) were tested to compare the U-value through the wall. The brick with the low U-value was used to build a composite wall with the addition of glass wool and air cavity. The experiment was conducted maintaining different hot chamber temperatures (40℃ to 65℃) to see the effect of hot chamber temperature on the U-value. The temperature of the cold fluid chamber is increased by 0.9℃ in 0 to 210 min of testing duration. The maximum heat flux using Type-3 brick in the composite wall is reduced by 3.37%, and the percentage reduction in indoor temperature is 40.83%. Steady-state numerical analysis was also performed to perceive the temperature distribution on the composite wall surface and found to be well-matched with the experimental model with a percentage deviation of 1.96%. The results suggested that using glass wool and air cavities with low thermal conductivity, low density, and high specific heat capacity effectively reduces the U-value throughout the building wall.

Economic optimization of exterior wall insulation in Chinese office buildings by coupling artificial neural network and genetic algorithm

Envelope insulation is an essential measure to reduce building energy consumption and is the focus of many building energy codes. In China, the current standard (GB 55015-2021) only specified limit values for the thermal conductivity (U) of the exterior walls, and it ignored variations in building characteristics and internal parameters. Therefore, it is difficult for architects to quickly determine the exterior wall U-value in building design and retrofit. To address this issue, the exterior wall U-values of office buildings in China were economically optimized by TRNSYS, ANN, and GA. To begin with, the effect of enhancing exterior wall insulation on the heating and cooling energy consumption of office buildings was investigated by TRNSYS software, taking into account variations in building characteristics. Moreover, the exterior wall U-values of office buildings were optimized by coupling ANN and GA. The results showed that the design values of the GB 55015-2021 did not achieve the optimal results in China, and the most economical exterior wall U-value was 0.24 W/(m2·K) in Harbin, 0.24 W/(m2·K) in Beijing, 0.42 W/(m2·K) in Changsha, 1.82 W/(m2·K) in Guangzhou, 0.66 W/(m2·K) in Kunming. Compared with the pre-optimization, the annual life cycle cost of building heating and cooling reduced by 0.96 % in Harbin, 2.07 % in Beijing, 1.44 % in Changsha, 1.68 % in Guangzhou, and 7.37 % in Kunming. In addition, variations in building parameters should be considered in the design phase of new buildings or in the renovation of old buildings, such as internal heat gain, which may lead to different optimal results.

Improvement of the thermal performance of hollow clay bricks for structural masonry walls

Although structural masonry walls are widely used in construction, achieving lower U-value is crucial to minimize energy losses and greenhouse gas emissions. The effect on the U-value of hollow clay masonry walls is evaluated by modifying the clay and mortar thermal conductivities, as well as the brick grid and thickness. Heat transfer through bricks and walls was modeled using a 3D-finite element method while model validation was based on experimental tests. Smaller rectangular cavities reduce the U-value to 0.761 W/m2K; increasing the brick thickness reduces the U-value to 0.563 W/m2K. Moreover, reducing the clay thermal conductivity showed negligible reductions in the wall U-value.

Optimization of exterior wall insulation in office buildings based on wall orientation: Economic, energy and carbon saving potential in China

The aim of this study is to optimize the exterior wall insulation of different orientations to further reduce the heating and cooling energy consumption of office buildings, and to analyze its economic, energy and carbon saving potential in China. A six-story office building was selected as the case building and its energy consumption was evaluated using TRNSYS, considering variations in latitude, window-to-wall ratio, aspect ratio, and exterior wall U-values of four orientations. In addition, the exterior wall insulation in four directions was optimized by coupling artificial neural network and genetic algorithm. The main results showed that in the 20°N to 40°N region of China, the GA-optimized exterior wall U-values were highest in the southern direction, followed by the eastern and western directions, and finally the northern direction. In addition, overall, the optimal combination of exterior wall U-values showed a trend of high at low latitudes and low at high latitudes, and increased with increasing WWR and decreased with increasing AR. Finally, compared to the exterior wall requirements for energy efficient buildings in GB 55015-2021, the optimized office buildings at low altitude in China showed a 3.26 %, 6.77 % and 6.59 % reduction in ALCC, total energy consumption and total carbon emissions respectively.

Improving building resilience in the face of future climate uncertainty: A comprehensive framework for enhancing building life cycle performance

This research introduces an innovative resilient design framework, addressing gaps in building performance optimization by considering a holistic life cycle perspective and factoring in climate projection uncertainties from the 2020s to 2090s. The study concludes that in the case study, future climate scenarios in different Shared Socio-Economic Paths significantly impact building life cycle performance, with wall U-value, windows U-value, and wall density identified as major factors affecting building performance under future climate uncertainty. The ensemble learning model achieves high fidelity in predicting life cycle carbon emissions (LCCE), life cycle cost (LCC), and indoor discomfort hours (IDH) through input feature screening and hyperparameter optimization. Comparing optimization algorithms, Two-Archive Evolutionary Algorithm for Constrained multi-objective optimization (C-TAEA) demonstrates better convergence degree and optimization effect. Among the tested multi-criteria decision making methods, the VIKOR method selects the best building resilient design scheme from the Pareto data set, effectively reducing LCCE, LCC, and IDH, and leading to an overall improvement in building life cycle performance. Applying this framework, the finalized scheme for an office building in cold region optimizes 44.0% of LCCE, 8.2% of LCC, and 4.3% of IDH. This framework develops a new approach to improve building life cycle performance under future climate uncertainty and supports informed decision-making for resilient building design. To promote carbon neutrality in construction, it is urged that future studies incorporate additional sources of uncertainty, such as occupant behavior, into the proposed framework.

Simulation-based sensitivity analysis of energy performance applied to an old Beijing residential neighbourhood for retrofit strategy optimisation with climate change prediction

Due to the high energy consumption in the building sector and the ever-increasing urbanisation rate, the demand for retrofitting old buildings in urban areas is increasing in China, especially in metropolitan cities like Beijing. However, despite national and local policies calling for extensive building retrofitting, it is a challenge to determine a cost-effective retrofit strategy. Considering this, the study establishes a novel approach that analyses the sensitivity of building energy consumption on parameters defining the materials used on the building envelope, as well as the solar shading and airtightness of the building. This research builds EnergyPlus models using geometric data captured from the map, building fabric data from local design standards, and a set of varying activity schedules, and carries out simulations to calculate the building energy consumption of a residential neighbourhood in Beijing, China. The energy consumption data is then used for a sensitivity analysis using the Morris Method on 14 building envelope parameters in total. For different building shapes, the sensitivity analysis results highlight that the energy is most sensitive to infiltration, followed by window U-value and window SHGC. The solar absorptances and U-values of external walls and roofs are also found to have a moderate influence on total energy consumption. By using predicted weather files, this research further discusses the changing influences of these parameters considering climate change over the next few decades. The approach of this research is instructive for the analysis of buildings in other cities in cold climate regions due to the generalisability of the studied neighbourhood, and the result has the potential to inform the building management teams and policymakers to determine suitable retrofit strategies.

Evaluation of the Impact of Window Parameters on Energy Demand and CO2 Emission Reduction for a Single-Family House

This article deals with the determination of the impact of selected parameters on energy consumption for heating and cooling purposes and CO2 emissions. Mathematical modelling combined with planning a computational experiment was adopted as the research method. The database for creating the models was developed using building energy simulations performed with DesignBuilder software. A single-family house with an area of 101 m2 was the subject of this study. Four deterministic mathematical models for the estimation of annual energy demand for heating, cooling, total final energy demand, and CO2 emissions were developed. Four parameters affecting the energy balance of the house: the area of the glazing system (three levels), U-value of windows (two-, three- and four-pane), U-value of external walls (0.1, 0.15, 0.2 W/m2K) and location (Warsaw, Berlin, Paris) were considered. The article discusses in detail the influence of individual factors on the energy demand and their common interactions. It was found that the level of thermal insulation of the glazing system plays the most important role in saving energy. This factor was the only one to show a stable and significant reduction in house energy demand, and thus a reduction in CO2 emissions for all four objective functions.

Renovating buildings by modelling energy–CO2 emissions using particle swarm optimization and artificial neural network (case study: Iran)

Climate change is known as a serious threat to the human species, and its significance should be considered in building design. This study aims to investigate the relationship between energy consumption and CO2 emission in Iran during the years 2018–2019 using artificial neural networks (ANNs) and regression methods. The input data were gathered and optimized by the particle swarm optimization (PSO) algorithm. Lighting, equipment load rate, wall U-value, roof U-value and people density were deliberated as effective parameters. Afterwards, the ANN was created, trained and tested by the radial basis function (RBF) algorithm; also, the data were evaluated based on statistical analysis in SPSS software. The results demonstrated R2 = 0.99 and the 45-degree line for the predicted value. Energy consumption and CO2 were reduced to 35% and 73.21%, respectively. Furthermore, CO2 emissions and energy consumption had an inverse relationship with infiltration rates (−0.201) and (−0.098). Furthermore, CO2 emission and energy consumption had a linear relation in Iran with the equation of y = 1.63x + 0.52. Moreover, based on ANOVA test, R2 linear was 0.985 and R = 0.993, illustrating significant accuracy. Architects and designers could enjoy these findings as guidelines for renovation and designing purposes so as to alleviate the negative environmental impacts of construction.

Dual-objective building retrofit optimization under competing priorities using Artificial Neural Network

Building retrofit has received renewed interests in recent years, driven by energy-savings and indoor environmental quality goals. Digital technologies such as building performance simulation and optimization algorithms have been used to identify optimal retrofit schemes, yet the existing approaches are limited by the slow running speed of physics-based models and sub-optimal results. This study describes a novel framework, the Building Performance Optimization using Artificial Neural Network (BPO-ANN), which can automatically identify optimal building retrofit schemes. A robust Artificial Neural Network model was developed and validated as a surrogate to rapidly assess building performances, which was then connected to a genetic algorithm in search of Pareto optimal. The impact of key design attributes on building performances have been assessed using sensitivity analysis. The BPO-ANN framework has been tested in a high-performing campus building in Northern China under two competing objectives: building energy demand and occupant thermal comfort. It can automatically identify optimal design schemes, which were expected to achieve an energy-savings of 4% and reduce the annual thermal discomfort percentage by 4%. Sensitivity analysis suggested that window-to-wall ratio and HVAC setpoint have contributed the most to the performances of the campus building, followed by the roof U-value and wall U-value. The study has contributed methodologically to simulation-based optimization method, with novelties in the use of neural network algorithms to accelerate the otherwise time-consuming physics-based simulation models. It has also contributed a robust procedure in the tuning of hyperparameters in neural network models, with marked improvements in model prediction and computational efficiency.

Thermal Impact of 5G Antenna Systems in Sandwich Walls

The 5th generation (5G) cellular networks offer high speeds, low latency, and greater capacity, but they face greater penetration loss through buildings than 4G due to their higher frequency bands. To reduce this loss in energy-efficient buildings, a passive antenna system was developed and integrated into sandwich walls. However, the thermal effects of this system, which includes highly thermally conductive metals, require further study. In this research, three-dimensional heat transfer simulations were performed using COMSOL Multiphysics to determine the thermal transmittances (U-values) of 5G antenna walls. The results revealed that, using stainless steel as the connector material (current design), the U-value rose from 0.1496 (for the wall without antenna) to 0.156 W/m2K, leading to an additional heating loss per year of only 0.545 KWh/m2 in Helsinki. In contrast, with the previous design that used copper as the connector material, the U-value increased dramatically to 0.3 W/m2K, exceeding the National Building Code of Finland’s limit of 0.17 W/m2K and causing 12.8 KWh/m2 additional heat loss (23.5 times more than the current design). The current design significantly reduces thermal bridging effects. Additionally, three analytical methods were used to calculate antenna wall U-values: parallel paths, isothermal planes, and ISO 6946 combined. The isothermal planes method was found to be more accurate and reliable. The study also found that a wall unit cell with a single developed 5G antenna and a wall consisting of nine such cells arranged in a 3 × 3 grid pattern had the same U-values. Furthermore, areas affected by thermal bridging were typically smaller than the dimensions of a wall unit cell (150 mm × 150 mm).

Simulation of thermal comfort and energy demand in buildings of sub-Himalayan eastern India - Impact of climate change at mid (2050) and distant (2080) future

The global warming associated with climate change predicted by the Intergovernmental Panel on Climate Change (IPCC) is expected to deteriorate the indoor climate of free-running buildings. Features like proper orientation and wall thickness are important for the design of a building that is resilient to the impact of climate change. However, the implementation of these features is sometimes difficult, especially in a rugged hilly location. A whole building simulation was performed using DesignBuilder for an existing 3-storey free running multi-family concrete building located in the sub-Himalayan region of eastern India, for thermal comfort and energy demand during the present and the climate change scenarios of 2050 and 2080. The results show an increasing trend in the indoor operative temperature during the future climatic scenario, with the condition inside the top roof-exposed floor deteriorating the most. A decrease of 59.8% and 81.2% in the annual heating energy and an increase of 221.9% and 467.0% in the annual cooling energy were predicted for the future climate of 2050 and 2080 compared to the present. Parametric analysis performed considering orientation, wall U-value, infiltration rate and window-to-wall ratios revealed that the east/south-east facing orientation would perform the best with regard to overheating due to climate change. Further, the use of autoclaved aerated concrete (AAC) brick is recommended along with the decrease in air infiltration rate and window-to-wall ratio to improve the thermal performance of the indoor environment. In addition, we have also proposed a method to assess the under-cooling of an indoor environment.

Lightweight foamed concrete for houses in Jordan

Sufficient thermal insulation is fundamental to construct energy-efficient structures. As Jordan has little natural resources and applying insulation based on codes of thermal insulation are not put in force yet, the current practice for thermal insulation is investigated. A review of the progress change of modern architecture and construction in Jordan is presented. Then, a review of the structural use of foamed concrete is presented based on previous research and current research project and the applicability of the suggested foamed concrete in structural members is verified. In this paper, Artificial Neural Network is trained, tested, and to bridge the quantities of the components in mixture and the mechanical properties based on the current experimental program. The validation statistical evaluation measures (ASEvalidation=0.001586, MAREvalidation=25.586, and R2validation=0.97672) denote that ANN results and experimental outcomes are in well agreement. Lastly, three exterior walls are constructed based on conventional method used in Jordan using normal-weight concrete in one wall and two different foamed concrete mixtures in the others. The walls are tested for thermal insulation. Results shows that the U-values of walls with foamed concrete, namely; FC I and FC II walls are 36% and 47% lower than that of the wall casted with conventional concrete, respectively. This is expected to significantly save the annual energy by constructed external walls using these materials, which in turn would presents an attractive cost savings for clients.

Influence of using clay block with increassed mass on energy performance of an office builidng in Nis

The objective of the research was to compare various types of clay blocks in terms of construction thermal inertia parameters and the influence they would have on the energy performance of an office building located in Nis. For this, a new type of clay block with increased mass is proposed, and a custom approach for determining all relevant indicators is described, intensively relying on building energy performance simulations. Fourteen configurations of external walls made of clay blocks, including the newly proposed block with increased mass, were investigated using EnergyPlus with a custom weather file to obtain construction thermal storage indicators, i.e., time lag and decrement factor. The results show the average decrement factor of less than 1% and the average time lag of approximately 9 hours for the newly proposed clay block, which is very similar to the values obtained for commercially available clay blocks. In addition, the same model of the building was used to check the influence that this increased mass has on the energy performance of the building served by a low temperature radiant and fan coil system. The results indicate the possibility of reducing heating energy consumption by 3.65% by using the increased mass clay block, while maintaining similar wall U-values, when compared with regularly used clay blocks, with a negligible change in cooling energy consumption.

Multi-scale thermal, energetic and economic analysis of composite insulating materials made of silica aerogel in a fibrous inorganic mat

Composite materials made of silica aerogel in a fibrous inorganic mat, hereafter called “aerogel blankets”, represent promising insulating materials that can overcome the low mechanical resistance of monolithic aerogels, while preserving their very low thermal conductivity. Firstly, this paper details an experimentally validated mathematical model to predict the aerogel blankets’ effective thermal conductivity, as a function of the blankets' density, fiber volume fraction, and temperature. Secondly, an experimental test building retrofitted internally with a layer of needle glass fiber (NGF) aerogel blanket is monitored, and its thermal behavior is compared to the baseline case before the retrofit. The retrofitted wall U-value is derived from experimental data series using stationary and transient parametric methods. Then, a validated whole building Energy Plus numerical model is used to predict the annual energy consumption of a detached single-family house retrofitted with an NGF aerogel blanket, in the climates of Brussels (Belgium), Nice (France), and Stockholm (Sweden). Lastly, a 1-D COMSOL multiphysics model evaluates the economic profitability of installing the blanket as an interior thermal insulation system. Results show that the aerogel blankets have an effective thermal conductivity of about 0.0165 W.m−1.K−1. The designed mathematical model can reliably predict the gaseous conduction in the blanket, but deviations from the experimental measurements appear when considering the radiative and solid conduction components. The blanket's effective thermal conductivity increases with fiber volume fraction and temperature while its dependency curve to density is concave, with a minimum for densities of around 140 kg.m−3. Retrofitting a wall with 2.5 cm of NGF aerogel blanket as an internal insulation system almost doubles the thermal resistance of the experimental wall and decreases the heating load by more than 40 % in all tested climates. Internal thermal insulation with aerogel blankets is more profitable than conventional insulation materials, in places of high floor area price, because it can preserve the indoor living space.

Do high energy-efficient buildings increase overheating risk in cold climates? Causes and mitigation measures required under recent and future climates

Contradictory findings are reported in the literature showing that high energy-efficient buildings have either higher or lower overheating risks compared to old buildings. A methodology is developed using the Global and Local Sensitivity Analysis to quantify the contribution and correlation of individual building envelope parameter to the change in indoor operative temperature. This methodology is applied to an archetype Canadian detached house as a case study to evaluate its overheating risk. The building envelope thermal characteristics studied represent houses built in different periods from 1950 to high energy-efficient buildings in Montreal under different weather generations: typical historical (1961–1990), recent observational (2016), and typical future years 2030 (2026–2045) and 2090 (2080–2099) generated based on RCP-4.5 and 8.5 scenarios.The results showed that the high energy-efficient buildings can be more resilient to climate change than old buildings if adequate ventilation is provided, where the decrease of window and wall U-value, and SHGC all contribute to the decrease in indoor temperature. While without adequate ventilation, the overheating risk in high-energy-efficient buildings can be higher than old buildings, where decreasing wall and window U-values and infiltration rate has a greater contribution to the increase of indoor temperature, while decreasing window SHGC has a lower contribution to the decrease in indoor temperature compared to the case with adequate ventilation. The results also showed that natural ventilation in the high energy-efficient buildings is sufficient to reduce the overheating risk under the current climate but will require additional interior and exterior shading under future climates.

Optimal Balance between Heating, Cooling and Environmental Impacts: A Method for Appropriate Assessment of Building Envelope’s U-Value

In Europe, the recent application of regulations oriented to zero-energy buildings and climate neutrality in 2050 has led to a reduction in energy consumption for heating and cooling in the construction sector. The thermal insulation of the building envelope plays a key role in this process and the requirements about the maximum allowable thermal transmittance are defined by country-specific guidelines. Typically, high insulation values provide low energy consumption for heating; however, they may also entail a risk of overheating in summer period and thus negatively affect the overall performance of the building. In addition, the embodied energy and related emissions caused by the manufacturing and transportation processes of thermal insulation cannot be further neglected in the evaluation of the best optimal solution. Therefore, this paper aims to evaluate the influence in terms of embodied and operational energy of various walls’ thermal insulation thicknesses on residential buildings in Europe. To this end, the EnergyPlus engine was used for the energy simulation within the Ladybug and Honeybee tools, by parametrically conducting multiple iterations; 53 variations of external wall U-value, considering high- and low-thermal-mass scenarios, were simulated for 100 representative cities of the European context, using a typical multifamily building as a reference. The results demonstrate that massive walls generally perform better than lightweight structures and the best solution in terms of energy varies according to each climate. Accordingly, the wall’s thermal transmittance for the samples of Oslo, Bordeaux, Rome and Almeria representative of the Continental, oceanic temperate, Mediterranean, and hot, semi-arid climates were, respectively: 0.12, 0.26, 0.42, and 0.64 W/m2K. The optimal solutions are graphically reported on the map of Europe according to specific climatic features, providing a guidance for new constructions and building retrofit.

Impacts of Courtyard Envelope Design on Energy Performance in the Hot Summer–Cold Winter Region of China

This paper aims to investigate the effects of courtyard envelope design on the energy performance of office buildings in the hot summer–cold winter region of China. Two types of courtyard buildings were simulated with 200 energy models by changing the following variables: window–wall ratio (south, north, east, and west walls), window U-value, wall U-value, solar heat gain coefficient, and orientation. The treed Gaussian process (TGP) sensitivity analysis method was employed to quantify the contributions of parameters related to courtyard design that result in the changes of annual cooling, heating, lighting, and total energy consumption. The results show that the courtyard envelope design has a significant effect on the energy consumption in this case study. The most influential factor affecting annual cooling energy consumption and the annual total energy consumption is the orientation, which is responsible for 37% and 81%, respectively, in two layouts to annual cooling energy use. The corresponding proportion increased to 45% and 86% in terms of the total energy consumption. The most influential factor influencing annual heating energy use is the window U-value, which explained nearly 60% of the changes to the heating energy use. The effect of the solar heat gain coefficient in two kinds of layouts is as high as 82% and 79% for lighting energy use in this case study. Due to the different courtyard forms, the magnitude of the effect of the parameters on energy consumption and the main trend of the effect is slightly different.

The use of statistical and machine learning tools to accurately quantify the energy performance of residential buildings.

Prediction of building energy consumption is key to achieving energy efficiency and sustainability. Nowadays, the analysis or prediction of building energy consumption using building energy simulation tools facilitates the design and operation of energy-efficient buildings. The collection and generation of building data are essential components of machine learning models; however, there is still a lack of such data covering certain weather conditions. Such as those related to arid climate areas. This paper fills this identified gap with the creation of a new dataset for energy consumption of 3,840 records of typical residential buildings of the Saudi Arabia region of Qassim, and investigates the impact of residential buildings’ eight input variables (Building Size, Floor Height, Glazing Area, Wall Area, window to wall ratio (WWR), Win Glazing U-value, Roof U-value, and External Wall U-value) on the heating load (HL) and cooling load (CL) output variables. A number of classical and non-parametric statistical tools are used to uncover the most strongly associated input variables with each one of the output variables. Then, the machine learning Multiple linear regression (MLR) and Multilayer perceptron (MLP) methods are used to estimate HL and CL, and their results compared using the Mean Absolute Error (MAE), the Root Mean Square Error (RMSE), and coefficient of determination (R2) performance measures. The use of the IES simulation software on the new dataset concludes that MLP accurately estimates both HL and CL with low MAE, RMSE, and R2, which evidences the feasibility and accuracy of applying machine learning methods to estimate building energy consumption.