Evaluation of regional comprehensive development efficiency under low-carbon policy: based on optimized DDF-GML combined with unsupervised clustering method.

In the study of urban development, it is very important to evaluate the influence of production factors reasonably and efficiently for the region to achieve efficient development. The principal aim of this investigation is to amalgamate the conventional measurement model characterized by robust interpretability with the non-parametric model characterized by limited interpretability, thereby enhancing the precision of research outcomes. Towards this objective, the study employs an optimized directional distance function integrated with a global Malmquist-Luenberger index to formulate a comprehensive total factor productivity measurement framework. In elucidating the homogeneous attributes of regions, departing from prior methodologies reliant on manual or direct algorithmic partitioning, this paper employs the K-means clustering algorithm for index discernment, abstracting the concept of K-means clustering centroids to encapsulate regional homogeneity, thereby delineating results through the visualization of regional development potential maps and the evolution of centroid-based clustering trend maps. The findings of the investigation illuminate common patterns of change across disparate regions, proposing a strategy for leveraging regional resource endowments towards a cohesive framework, thereby transcending constraints imposed by production efficiency limitations. Amidst the backdrop of the COVID-19 pandemic, this study draws upon provincial-level data spanning from 2000 to 2018 in China. The conclusive analytical outcomes underscore the pivotal role of energy factors in regional development efficiency, particularly within high-potential development regions, followed by the capital and labor factors. Concurrently, the study discerns a discernible hierarchical pattern among areas of development potential, which exhibits correlation with factor mobility dynamics.

Cu Based Dilute Alloys for Tuning the C2+ Selectivity of Electrochemical CO2 Reduction.

Electrochemical CO2 reduction is a promising technology for replacing fossil fuel feedstocks in the chemical industry but further improvements in catalyst selectivity need to be made. So far, only copper-based catalysts have shown efficient conversion of CO2 into the desired multi-carbon (C2+) products. This work explores Cu-based dilute alloys to systematically tune the energy landscape of CO2 electrolysis toward C2+ products. Selection of the dilute alloy components is guided by grand canonical density functional theory simulations using the calculated binding energies of the reaction intermediates CO*, CHO*, and OCCO* dimer as descriptors for the selectivity toward C2+ products. A physical vapor deposition catalyst testing platform is employed to isolate the effect of alloy composition on the C2+/C1 product branching ratio without interference from catalyst morphology or catalyst integration. Six dilute alloy catalysts are prepared and tested with respect to their C2+/C1 product ratio using different electrolyzer environments including selected tests in a 100-cm2 electrolyzer. Consistent with theory, CuAl, CuB, CuGa and especially CuSc show increased selectivity toward C2+ products by making CO dimerization energetically more favorable on the dominant Cu facets, demonstrating the power of using the dilute alloy approach to tune the selectivity of CO2 electrolysis.

Clustered Routing Using Chaotic Genetic Algorithm with Grey Wolf Optimization to Enhance Energy Efficiency in Sensor Networks.

As an alternative to flat architectures, clustering architectures are designed to minimize the total energy consumption of sensor networks. Nonetheless, sensor nodes experience increased energy consumption during data transmission, leading to a rapid depletion of energy levels as data are routed towards the base station. Although numerous strategies have been developed to address these challenges and enhance the energy efficiency of networks, the formulation of a clustering-based routing algorithm that achieves both high energy efficiency and increased packet transmission rate for large-scale sensor networks remains an NP-hard problem. Accordingly, the proposed work formulated an energy-efficient clustering mechanism using a chaotic genetic algorithm, and subsequently developed an energy-saving routing system using a bio-inspired grey wolf optimizer algorithm. The proposed chaotic genetic algorithm-grey wolf optimization (CGA-GWO) method is designed to minimize overall energy consumption by selecting energy-aware cluster heads and creating an optimal routing path to reach the base station. The simulation results demonstrate the enhanced functionality of the proposed system when associated with three more relevant systems, considering metrics such as the number of live nodes, average remaining energy level, packet delivery ratio, and overhead associated with cluster formation and routing.

Can forests realize the carbon neutrality dream? Evidence from a global sample.

The enlarge in economic activities and the urban population at the global level has brought about an increase in the demand for energy, food, and natural resources, as well as an exacerbation in global climate change concerns. In this respect, it is important to ensure the balance between global climate change and global economic activities. Therefore, a wide literature has emerged that searches for alternative solutions to improve climate change and carbon dioxide (CO2) emissions. The majority of existing studies emphasize the importance of renewable energy sources in environmental improvement efforts. Few studies highlight the importance of forestation in environmental improvement efforts, highlighting the non-linear effects of forestation. To fill this gap, this study uses panel data from 181 countries between 1990 and 2022 and evaluates the non-linear impact of economic growth, forest extent, energy efficiency, and urban growth on per capita CO2 emissions using a dynamic panel threshold and dynamic panel quantile threshold methods. Furthermore, we extend the model and conduct robustness tests examining the non-linear threshold effects of renewable and non-renewable energy consumption on per capita CO2 emissions. Our findings provide pieces of evidence that forest extents are an alternative solution to renewable energy use and energy efficiency in environmental improvement efforts.

CVD Grown CNTs-Modified Electrodes for Vanadium Redox Flow Batteries.

Vanadium redox flow batteries (VRFBs) are of considerable importance in large-scale energy storage systems due to their high efficiency, long cycle life and easy scalability. In this work, chemical vapor deposition (CVD) grown carbon nanotubes (CNTs)-modified electrodes and Nafion 117 membrane are utilised for formulating a vanadium redox flow battery (VRFB). In a CVD chamber, the growth of CNTs is carried out on an acid-treated graphite felt surface. Cyclic voltammetry of CNT-modified electrode and acid-treated electrode revealed that CNTs presence improve the reaction kinetics of V3+/V2+ and VO2+/VO2+ redox pairs. Battery performance is recorded for analysing, the effect of modified electrodes, varying electrolyte flow rates, varying current densities and effect of removing the current collector plates. CNTs presence enhance the battery performance and offered 96.30% of Coulombic efficiency, 79.33% of voltage efficiency and 76.39% of energy efficiency. In comparison with pristine electrodes, a battery consisting CNTs grown electrodes shows a 14% and 15% increase in voltage efficiency and energy efficiency, respectively. Battery configured without current collector plates performs better as compared to with current collector plates which is possibly due to decrease in battery resistance.

Application of triple-branch artificial neural network system for catalytic pellets combustion.

On the international level, it is common to act on reducing emissions from energy systems. However, in addition to industrial emissions, low-stack emissions also make a significant contribution. A good step in reducing its environmental impact, is to move to biofuels, including biomass. This paper examines the impact of placing a catalytic system in a retort boiler to minimize emissions of greenhouse gases, dust and other pollutants when burning pellets. The effect of platinum, and oxides of selected metals placed on the deflector as a solid catalyst was studied. Based on the experimental data, a branched artificial neural network was constructed and trained. The routing of three parallel topologies made it possible to achieve high accuracy while keeping the input data relatively simple. The system showed an average error of 3.54% against arbitrary test data. On the basis of experimental data as well as predictions returned by the artificial neural network, recommendations were shown for the catalysts used and their amounts. Depending on the biomass from which the pellet was produced, the experiment suggested the use of titanium or copper oxides. In the case of the neural network, it was able to select a better system, based on platinum, improving emission reductions by up to more than 19%, depending on the type of pellet used.

Factor mismatch measurement of resource-based cities: Evidence from all-round optimization total factor productivity decomposition framework.

In-depth analysis of the factors affecting the transformation of resource-based cities can provide effective support for the transformation and development of resource-dependent regions. How to comprehensively identify the factors affecting the transformation of resource-based cities is a complex problem. This study starts from the total factor productivity model and focuses on the two core basic factors that affect the transformation process of cities reliant on resources. Economic benefits and energy efficiency, respectively, from the economic benefit analysis framework and energy efficiency analysis framework for reconstruction, the two frameworks are combined with the use of distorted prices of resource elements to solve the problem that the synergistic effect of economic benefits and energy efficiency can not be measured. In order to quantitatively analyze the factors that affect the development efficiency of cities reliant on resources under the single or synergistic effect of the comprehensive framework, this study optimizes the directional distance function from three perspectives: exogenous weight, direction vector endogeneity, and absolute distance transformation relative distance, thus achieving an accurate assessment transformation efficiency of cities reliant on resources. Considering the impact of the new coronavirus epidemic, this study only selected the data of resource-based cities from 2003 to 2018, and found through model calculation that the impact on the transformation of cities reliant on resources: (1) Labor mismatch is mainly achieved by affecting the structure about the production of resource-based enterprises and industrial human resources; (2) Capital mismatch is mainly realized by affecting the production of resource-based enterprises; (3) Energy mismatch is mainly achieved by affecting high energy consumption enterprises and low production technology level enterprises. Further research shows that the main objects of these factors are the four parts of production technology level, energy consumption, total factor productivity and industrial structure. Through these contents, they affect environmental efficiency and deeply affect the transformation process of resource-based cities.

Powering the Future Green Buildings: Multifunctional Ultraviolet-Shielding Transparent Wood.

Indoor UV damage is a serious problem that is often ignored. Common glasses cannot filter UV rays well and have fragility and environmental issues. UV-shielding transparent wood (TW) holds promise, yet striking the right balance between blocking UV rays and allowing sufficient visible-light transmission poses a challenge. The pronounced capillary force, fueled by persistent moisture and extractives in wood, alongside the existence of multiphase interfaces, collectively hinder the uniform penetration of polymers and the effective dispersion of nanomaterials within the wood skeleton. Here, we incorporate high-pressure supercritical CO2 fluid-assisted impregnation (HSCFI) into fabricating UV-shielding TW. The supercritical CO2 pretreatment efficiently eliminates moisture and refines wood structure by extracting polar substances, resulting in a prominent 52.4% increase in average water permeability. Subsequently, this HSCFI method facilitates the infiltration of methyl methacrylate (MMA) monomer and Ce-ZnO nanorods (NRDs) into the refined anhydrous wood, leveraging the excellent solvency of supercritical CO2 for MMA. The impregnation rate of PMMA undergoes a substantial increase from 34.5 to 59.1%. With the robust UV-blocking capability of Ce-ZnO NRDs, thanks to dual-valence Ce doping widening the ZnO energy gap via the Burstein-Moss effect and their unique photoactive microstructure featuring a solid prism with a sharp hexahedral pyramidal tip, along with intrinsic physical scattering/reflection actions, Ce-ZnO NRDs@TW achieves an impressive 99.6% UVA radiation blockage (the highest for TW) and maintains high visible-light transmission (83.2%). Furthermore, Ce-ZnO NRDs@TW presents favorable energy-saving, sound absorption, and antifungal abilities, making it a promising candidate for future green buildings.

A puzzle on environmental quality and energy security.

i) This study examines the determinants of environmental quality. It is not possible to fully analyze the complex network that emerges from the set of interactions of these determinants, both with each other and with environmental security. Indeed, a number of variables and relationships hidden in the background of the puzzle such as 'game theoretical interactions between economies on energy security', characterize this network. However, this study, which includes energy security and environmental quality simultaneously, may open the door to revealing the key patterns of the current network. ii) This study, which investigates the network between environmental problems and energy security, provides empirical evidence that these two variables may well evolve by positively affecting each other under some conditions. iii) Using the current and sophisticated econometric methods such as CDw + based on Juodis and Reese (2022) test and CS-ARDL Model, over a panel of top 20 energy-using countries in the period 1980-2018, the empirical analysis of the article shows that an increase in energy security risk positively affects environmental quality in aggregate by motivating increased energy efficiency, triggering environmental awareness and regulations, and stimulating research and development activities for clean energy etc. Technologies. Therefore, this study concludes that potential policies and reforms, including reducing fossil fuel consumption, increasing energy efficiency in distribution and consumption, encouraging investments in clean energy are of key importance in making energy security sustainable in the long term by increasing environmental quality.

Urban green total factor energy efficiency and its decomposition under a multidimensional heterogeneity framework: a case study of the Yellow River Basin, China.

Assessing and monitoring the green total factor energy efficiency (GTFEE) of cities while considering technology heterogeneity is crucial for the development of energy-conservation and emission-reduction policies. Considering that the heterogeneity of production technologies encompasses several dimensions, this paper proposes a 3E3S (Economy-Environment-Energy-Society-Science-Space) heterogeneity framework and integrates it with the improved meta-frontier global SBM-undesirable to analyze GTFEE and its decomposition. Empirical analysis of cities in the Yellow River Basin of China (YRBC) highlights the following: (1) The 3E3S heterogeneity framework facilitates the classification of all cities into three distinct groups, a finding that contrasts significantly with previous outcomes documented in the literature that relied solely on criteria such as geographic location. (2) The three groups identified under the meta-frontier exhibit substantial energy-saving potentials of 24.49%, 35.17%, and 52.46%, respectively. Additionally, there are spatiotemporal variations in GTFEE, with cities located in the central part of YRBC, particularly those in Shanxi province, demonstrating poor GTFEE performance. (3) The decomposition analysis of GTFEE indicates that technological progress plays a pivotal role in enhancing GTFEE on the whole, albeit with varying approaches for improving GTFEE depending on individual cities.

Towards a low-carbon and beautiful world: assessing the impact of digital technology on the common benefits of pollution reduction and carbon reduction.

When ecology thrives, civilization thrives, and when ecology declines, civilization declines. Based on panel data from 30 provinces in China from 2000 to 2021, this study used marginal abatement costs to estimate the co-benefits of pollution reduction and carbon reduction. Two-way fixed effect and two-stage intermediary effect models were used to evaluate the impact of digital technology on co-benefits and its indirect channels. The results indicated that China's total carbon emissions maintained a steady growth trend, while air pollution showed a fluctuating declining trend. Reaching peak carbon neutrality calls for more innovative solutions. Under joint emission reduction efforts, the study revealed marginal abatement cost savings of 535.8 million yuan/million tons and 6216.5 million yuan/µg/m3 for carbon reduction and pollution reduction, respectively. Most importantly, the study confirmed that joint emission reduction programs can reduce environmental governance costs more than individual emission reductions can, and the co-benefits increased from 37.983 to 44.757. The co-benefits generally showed a trend of fluctuation and increases and had the characteristics of phased transformation. Intragroup differences and cross-overlapping between regions made regional differences in co-benefits obvious. The subversive, permeable, and integrated features of digital technology have resulted in the all-around transformation of the economy and society, and the new technology-economy paradigm has significantly improved co-benefits. The conclusion remains valid after robustness testing and controlling for endogeneity problems. The results of the mechanism analysis suggest that digital technology can indirectly improve synergies through the intermediary channels of fostering green technology innovation, reducing energy consumption intensity and improving the energy structure.

The impact of photovoltaic self-consumption on the daily electricity demand in Spain: Definition of a model to estimate it.

Introduction: A radical shift in energy production is underway worldwide, replacing fossil fuels with renewable sources and causing structural changes in power generation systems. Problem statement: Photovoltaic installations for self-consumption have experienced a steep increase in recent years. They have reached a significant installed capacity to cause a noticeable reduction in consumption from the national grid, which can cause serious management problems. Objectives: In this work, the evolution of the Spanish demand in the last years is analyzed to identify the influence of self-consumption in the overall demand. In addition, a mathematical model is defined to estimate this influence. Methodology: The demand curves of equivalent days in years with high and low installed self-consumption photovoltaic systems have been compared. Then, an estimation of the electricity generated with this source is proposed, with a mathematical model that takes into account data on solar radiation, installed photovoltaic power for self-consumption and other relevant factors. Results: The analysis of the demand has shown a significant reduction of the electricity demand in daylight hours when the number of self-consumption photovoltaic systems increases. Moreover, the proposed model has been able to provide an estimation of the electricity generated with this source. The addition of these estimates to the actual consumption curves of years with a high number of self-consumption installations gives profiles close to those obtained when self-consumption was low. Recommendation: New storage systems need to be implemented and grid management need to be improved to take advantage of the surpluses produced by photovoltaic systems.

Investigation of thermal alkaline pretreatment of primary and waste activated sludge on the energy efficiency of sludge digestion.

Because of the naturally limited anaerobic degradability and limited biogas yield of raw sludge (RS), this study aims to increase the biogas production of primary sludge (PS) and waste activated sludge (WAS) by the integration of thermal alkaline process (TAP). PH 11 is confirmed to be the most suitable pH value for the TAP of both sludges. Moreover, with the pretreatment at pH 11 and 160 °C (6 bar) for 30 min, the investigated PSs and WASs achieved an increased biogas production of up to 81 % and 72 %, respectively. The improved net electricity production of WASs after TAP varied between 15-43 % compared to conventional WAS digestion. However, the TAP of PS at pH 11 enhanced the biogas production by 1-81 %, which did not constantly contribute to an improved net electricity production.

Designing energy-efficient buildings in urban centers through machine learning and enhanced clean water managements.

Rainwater Harvesting (RWH) is increasingly recognized as a vital sustainable practice in urban environments, aimed at enhancing water conservation and reducing energy consumption. This study introduces an innovative integration of nano-composite materials as Silver Nanoparticles (AgNPs) into RWH systems to elevate water treatment efficiency and assess the resulting environmental and energy-saving benefits. Utilizing a regression analysis approach with Support Vector Machines (SVM) and K-Nearest Neighbors (KNN), this study will reach the study objective. In this study, the inputs are building attributes, environmental parameters, sociodemographic factors, and the algorithms SVM and KNN. At the same time, the outputs are predicted energy consumption, visual comfort outcomes, ROC-AUC values, and Kappa Indices. The integration of AgNPs into RWH systems demonstrated substantial environmental and operational benefits, achieving a 57% reduction in microbial content and 20% reductions in both chemical usage and energy consumption. These improvements highlight the potential of AgNPs to enhance water safety and reduce the environmental impact of traditional water treatments, making them a viable alternative for sustainable water management. Additionally, the use of a hybrid SVM-KNN model effectively predicted building energy usage and visual comfort, with high accuracy and precision, underscoring its utility in optimizing urban building environments for sustainability and comfort.

A dSPACE-based implementation of ANFIS and predictive current control for a single phase boost power factor corrector.

This paper presents an innovative control scheme designed to significantly enhance the power factor of AC/DC boost rectifiers by integrating an adaptive neuro-fuzzy inference system (ANFIS) with predictive current control. The innovative control strategy addresses key challenges in power quality and energy efficiency, demonstrating exceptional performance under diverse operating conditions. Through rigorous simulation, the proposed system achieves precise input current shaping, resulting in a remarkably low total harmonic distortion (THD) of 3.5%, which is well below the IEEE-519 standard threshold of 5%. Moreover, the power factor reaches an outstanding 0.990, indicating highly efficient energy utilization and near-unity power factor operation. To validate the theoretical findings, a 500 W laboratory prototype was implemented using the dSPACE ds1104 digital controller. Steady-state analysis reveals sinusoidal input currents with minimal THD and a power factor approaching unity, thereby enhancing grid stability and energy efficiency. Transient response tests further demonstrate the system's robustness against load and voltage fluctuations, maintaining output voltage stability within an 18 V overshoot and a 20 V undershoot during load changes, and achieving rapid response times as low as 0.2 s. Comparative evaluations against conventional methods underscore the superiority of the proposed control strategy in terms of both performance and implementation simplicity. By harnessing the strengths of ANFIS-based voltage regulation and predictive current control, this scheme offers a robust solution to power quality issues in AC/DC boost rectifiers, promising substantial energy savings and improved grid stability. The results affirm the potential of the proposed system to set new benchmarks in power factor correction technology.

Decreasing Intracellular Entropy by Increasing Mitochondrial Efficiency and Reducing ROS Formation-The Effect on the Ageing Process and Age-Related Damage.

A hypothesis is presented to explain how the ageing process might be influenced by optimizing mitochondrial efficiency to reduce intracellular entropy. Research-based quantifications of entropy are scarce. Non-equilibrium metabolic reactions and compartmentalization were found to contribute most to lowering entropy in the cells. Like the cells, mitochondria are thermodynamically open systems exchanging matter and energy with their surroundings-the rest of the cell. Based on the calculations from cancer cells, glycolysis was reported to produce less entropy than mitochondrial oxidative phosphorylation. However, these estimations depended on the CO2 concentration so that at slightly increased CO2, it was oxidative phosphorylation that produced less entropy. Also, the thermodynamic efficiency of mitochondrial respiratory complexes varies depending on the respiratory state and oxidant/antioxidant balance. Therefore, in spite of long-standing theoretical and practical efforts, more measurements, also in isolated mitochondria, with intact and suboptimal respiration, are needed to resolve the issue. Entropy increases in ageing while mitochondrial efficiency of energy conversion, quality control, and turnover mechanisms deteriorate. Optimally functioning mitochondria are necessary to meet energy demands for cellular defence and repair processes to attenuate ageing. The intuitive approach of simply supplying more metabolic fuels (more nutrients) often has the opposite effect, namely a decrease in energy production in the case of nutrient overload. Excessive nutrient intake and obesity accelerate ageing, while calorie restriction without malnutrition can prolong life. Balanced nutrient intake adapted to needs/activity-based high ATP requirement increases mitochondrial respiratory efficiency and leads to multiple alterations in gene expression and metabolic adaptations. Therefore, rather than overfeeding, it is necessary to fine-tune energy production by optimizing mitochondrial function and reducing oxidative stress; the evidence is discussed in this paper.

Unlocking a greener future: The role of digital finance in enhancing green total factor energy efficiency.

The development of digital finance provides new opportunities for improving energy efficiency and promoting green development. This paper calculates green total factor energy efficiency (GTFEE) using the super-efficiency SBM model and examines the impact of digital finance on GTFEE. Digital finance has a significant positive impact on GTFEE. Under a bank-dominated financial structure, the positive impact of digital finance on GTFEE is quite significant. In regions with intense banking competition, a large amount of green credit, and lower resource dependence, digital finance is conducive to enhancing GTFEE. Optimizing the allocation efficiency of production factors is an essential mechanism for digital finance to encourage improvements in GTFEE. Digital finance alleviates distortions in factor markets and enhances the matching of the marginal output and the price of capital, labor, and energy factors, thereby facilitating improvements in GTFEE. Further analysis indicates that digital finance has a significant, positive spatial spillover effect on GTFEE, enhancing GTFEE levels in both local and neighboring regions. This study enriches the research on the relationship between digital finance and energy efficiency and provides theoretical foundations and policy references for how digital finance can better serve the green transition of the economy.

Cross-Domain Knowledge Transfer for Sustainable Heterogeneous Industrial Internet-of-Things Networks.

In this article, a novel cross-domain knowledge transfer method is implemented to optimize the tradeoff between energy consumption and information freshness for all pieces of equipment powered by heterogeneous energy sources within smart factory. Three distinct groups of use cases are considered, each utilizing a different energy source: grid power, green energy source, and mixed energy sources. Differing from mainstream algorithms that require consistency among groups, the proposed method enables knowledge transfer even across varying state and/or action spaces. With the advantage of multiple layers of knowledge extraction, a lightweight knowledge transfer is achieved without the need for neural networks. This facilitates broader applications in self-sustainable wireless networks. Simulation results reveal a notable improvement in the 'warm start' policy for each equipment, manifesting as a 51.32% increase in initial reward compared to a random policy approach.

Insight into the surface discharge cold plasma efficient inactivation of Pseudomonas fluorescens in water based on exogenous reactive oxygen and nitrogen species: Synergistic mechanism and energy benefits.

As well known, surface discharge cold plasma has efficient inactivation ability and a variety of RONS are main active particles for inactivation, but their synergistic mechanism is still not clear. Therefore, surface discharge cold plasma system was applied to treat Pseudomonas fluorescens to study bacterial inactivation mechanism and energy benefit. Results showed that energy efficiency was directly proportional to applied voltage and inversely proportional to initial concentration. Cold plasma treatment for 20 min was inactivated by approximately > 4-log10Pseudomonas fluorescens and application of •OH and 1O2 scavengers significantly improved survival rate. In addition, •OH and 1O2 destroyed cell membrane structure and membrane permeability, which promoted diffusion of RONS into cells and affecting energy metabolism and antioxidant capacity, leading to bacterial inactivation. Furthermore, accumulation of intracellular NO and ONOOH was related to infiltration of exogenous RNS, while accumulation of •OH, H2O2, 1O2, O2- was the result of joint action of endogenous and exogenous ROS. Transcriptome analysis revealed that different RONS of cold plasma were responsible for Pseudomonas fluorescens inactivation and related to activation of intracellular antioxidant defense system and regulation of genes expression related to amino acid metabolism and energy metabolism, which promoting cellular process, catalytic activity and other biochemical pathways.

Estimating the energy consumption for residential buildings in semiarid and arid desert climate using artificial intelligence.

This research aims to develop predictive models to estimate building energy accurately. Three commonly used artificial intelligence techniques were chosen to develop a new building energy estimation model. The chosen techniques are Genetic Programming (GP), Artificial Neural Network (ANN), and Evolutionary Polynomial Regression (EPR). Sixteen energy efficiency measures were collected and used in designing and evaluating the proposed models, which include building dimensions, orientation, envelope construction materials properties, window-to-wall ratio, heating and cooling set points, and glass properties. The performance of the developed models was evaluated in terms of the RMS, R2, and MAPE. The results showed that the EPR model is the most accurate and practical model with an error percent of 2%. Additionally, the energy consumption was found to be mainly governed by three factors which dominate 87% of the impact; which are building size, Solar Heating Glass Coefficient (SHGC), and the target inside temperature in summer.