Total Electricity Consumption Research Articles

Retrofitting of a solar cooling and heating plant by employing PCM storage and adjusting control strategy

A solar cooling and heating plant with a 1000 l hot water storage tank has been operated since 2018. Two main problems were observed: (1) a fluctuating operation of the absorption chiller due to a high number of stat-up and shut-down procedures; (2) high electricity consumption caused by a low solar fraction SFn of 50.9% during the cooling period. In 2021, the solar plant was retrofitted by employing a shell and tube heat exchanger based on a PCM melting at 64 °C and by adjusting the control strategy. The system performance was evaluated and compared before and after retrofitting based on the operational data in 2021 and 2022. The total number of switch-on times of the chiller shrank by 33.5% and the operation time was increased by 65% in 2022 due to the stable hot water inlet temperature. Contributing to the high heat storage capacity of the PCM storage, more solar thermal energy was utilized and the average SFn reached 75.6% during the cooling period in 2022, leading to a 24.4% reduction in the total electricity consumption compared to 2021.

Analysis and Forecasting of Energy Data and GDP Based on Pearson Correlation Analysis-Random Forest Algorithm

The relationship between energy data and GDP is an important economic issue because energy is the foundation that supports modern economic development. This paper examines the relationship between energy data and GDP and uses Pearson correlation analysis to calculate the relationship between each variable in energy data and annual GDP for 52 regions. The results show that total electricity consumption is the factor with the highest degree of influence on GDP, followed by natural gas consumption, biomass consumption, hydroelectricity consumption, LPG consumption and geothermal energy consumption. This indicates that there is an extremely strong correlation between local energy data and GDP. To further explore this correlation, this paper attempts to predict the GDP of the locality based on the local energy data using a machine learning approach.The Random Forest algorithm is chosen as the machine learning model, and the training, validation and test sets are divided according to the ratio of 6:2:2. By using MSE, RMSE, MAE, MAPE and R to evaluate the model, it is found that the random forest machine learning algorithm can predict the local GDP well.The predicted value of GDP and the actual value of GDP are very close to each other, meanwhile, the value of MSE is 62.779.These findings show that the local energy data are closely related to economic development. The impact of energy factors on the economy should be taken into account when formulating economic policies. In addition, machine learning algorithms can provide strong support for economic forecasting and provide more accurate information for decision makers.

Performance study on an energy-efficient TSA air-purification process utilizing compression heat regeneration

A novel compression heat regeneration TSA process is disclosed in this paper, which aims to reduce the regeneration energy consumption of the raw air purifier in the air separation process. Since air separation is an energy-intensive process accounting for a significant proportion (about 5 %) of China's total electricity consumption, this new process holds great significance in energy conservation. Distinguishing from traditional processes, the new one integrates components such as a compressor, a regenerator, and an expander to meet the regeneration heat requirements of the purifier with minimal electrical energy consumption. It also recovers the waste heat from the tail gas to enhance the expander’s power generation, thereby realizing efficient and comprehensive energy utilization. Mathematical models have been established for components such as the adsorber, compressor, and expander, and simulations have been conducted based on actual data from an industrial-grade purifier. Consequently, historical parameter curves and bed profiles at the inlet and outlet of the adsorber have been obtained, providing insights into the purification performance and regeneration effectiveness of the new process. Furthermore, the inlet and outlet parameters, as well as the power consumption of the compressor and expander, have been determined. The temperature bed profiles and temperature history curves at the inlet and outlet of the regenerator have also been obtained. Subsequently, a comparison has been made between the new and traditional processes regarding energy consumption, and an economic assessment of the new process has been conducted based on its energy-saving potential. The following conclusions have been drawn through comparative analysis: (1) Compared to the traditional process, the new process can save approximately 51.6 % of electrical energy; (2) The payback period for retrofitting the traditional process to the new one is approximately 1.93 years.

Global optimization strategy of prosumer data center system operation based on multi-agent deep reinforcement learning

The escalating issues of high energy consumption and carbon emissions in data centers (DCs) necessitate the optimization of system operations. However, early optimization strategies were overly simplistic and lacked automated updating and iterative capabilities. With the evolution of artificial intelligence (AI), researchers have applied deep reinforcement learning (DRL) algorithms to system operations. However, the optimization focus has been limited to the internal systems, lacking global optimization. In this paper, a global optimization control strategy based on the Dueling double-deep Q network (D3QN) and value decomposition network (VDN) algorithms is proposed to make the DCs system operate more closely with the upstream, midstream, and downstream. By adjusting battery charging/discharging capacity, computational workload, and waste heat utilization heating temperature global synergistic optimization is achieved. Compared with without optimization, renewable energy waste, operation cost, total electricity consumption, and grid electricity consumption are reduced by 18.37%, 9.78%, 4.01%, and 29.74%, respectively. Additionally, a detailed comparison between non-algorithmic optimization and algorithmic optimization is provided, offering valuable insights for substantial energy savings and emissions reduction in DCs. The results demonstrate the importance of fully exploring the interactive potential between upstream energy supply, midstream computational workload, and downstream waste heat recovery to achieve synergistic global optimization of "computing power", "thermal energy" and "electrical energy" for the sustainable and green development of DCs or other prosumer buildings.

Low-carbon scheduling of electricity consumption in wastewater treatment plant by using photovoltaic system

Sewage treatment as a high energy consumption industry, its electricity consumption accounts for 3 % of the total electricity consumption of society. That means significant greenhouse gas emissions. In the context of China's goal of “reaching carbon peak by 2030 and achieving carbon neutrality by 2060”, reducing the energy consumption of wastewater treatment systems has emerged as an important issue in recent years. In this paper, the GPS-X simulation software was employed to conduct a simulation study of a modified Anoxic-Aerobic-Oxic wastewater treatment plant (WWTP) in Wuhan, and the response surface methodology (RSM) was utilized to ascertain the interactive effects of DO, IRF, ERR, and SD on the effluent quality, thereby identifying the operational parameters that minimize energy consumption while maintaining satisfactory effluent quality. Additionally, the PVsyst software was employed to design the solar power generation system of the WWTP and analyze its power generation potential. On this basis, through the coupling of photovoltaic power, electricity load, time-of-use pricing, and the water quality simulation model, and taking the WWTP data in September as a case study, the electricity usage strategies under various illumination conditions were formulated. The aim is to maximize the use of photovoltaic power to reduce the cost and carbon emissions of the WWTP. The results show that the optimal combination of operational parameters, including an external reflux ratio of 0.3, the internal recycle flow of 50,000 m3/d, and the sludge discharge of 448 m3/d, resulted in a reduction in power of 208.5 kW, and after the combination optimization of operational parameters and electricity utilization, the operation cost of the WWTP in September was reduced by 40 % ∼ 60 %, and the carbon emission attributable to electricity was reduced by 30 % ∼ 50 %.

Farm electricity system simulator (FESS): A platform for simulating electricity utilisation on dairy farms

The objective of this paper was to define, validate and demonstrate a model capable of accurately simulating dairy farm electricity consumption across varying herd and parlour sizes, to facilitate research investigating renewable energy systems (RES) and demand side management (DSM). The Farm Electricity System Simulator (FESS) was developed using grey-box modelling techniques utilizing empirical data for parameter tuning. Empirical data were gathered from nine spring calving, pasture based dairy farms located in the Republic of Ireland. A k-means clustering analysis was conducted, separating the farms into three, near homogenous groups, from which representative farms were selected. FESS was trained using 12 months of data from three representative farms using the repeat hold out method for data partitioning with 75 % of data used for training and 25 % used for validation. An optimisation algorithm was used to minimize the error during model training. Through cross-validation, FESS achieved a root mean squared error (RMSE) of 7.65 kWh, mean absolute percentage error (MAPE) of 7.10 %, mean percentage error (MPE) of −0.86 % and a relative prediction error (RPE) of 7.56 % for total daily electricity consumption. Across the three farms, the simulated outputs of FESS achieved an average R2 value of 0.72, demonstrating good agreement with observed data. FESS’s utility was demonstrated by analysing the effects of different electricity pricing structures and on-site solar photovoltaic electricity generation on total farm energy costs. We concluded that FESS simulated on-farm electricity consumption with sufficient accuracy for the intended application. FESS accurately simulated dairy farm electricity consumption across three dairy farms of different herd and parlour sizes while evaluating the effects of demand side management and renewable generation on farm electricity consumption and costs.

Impact of electric vehicle charging demand on power distribution grid congestion

California, a pioneer in EV adoption, has enacted ambitious electric vehicle (EV) policies that will generate a large burden on the state's electric distribution system. We investigate the statewide impact of uncontrolled EV charging on the electric distribution networks at a large scale and high granularity, by employing an EV charging profile projection that combines travel demand model, EV adoption model, and real-world EV charging data. We find a substantial need for infrastructure upgrades in 50% of feeders by 2035, and 67% of feeders by 2045. The distribution system across California must upgrade its capacity by 25 GW by 2045, corresponding to a cost between $6 and $20 billion. While the additional infrastructure cost drives the electricity price up, it is offset by the downward pressure from the growth of total electricity consumption and leads to a reduction in electricity rate between $0.01 and $0.06/kWh by 2045. We also find that overloading conditions are highly diverse spatially, with feeders in residential areas requiring twice as much upgrade compared to commercial areas. Our study provides a framework for evaluating EVs' impact on the distribution grid and indicates the potential to reduce infrastructure upgrade costs by shifting home-charging demand. The imminent challenges confronting California serve as a microcosm of the forthcoming obstacles anticipated worldwide due to the prevailing global trend of EV adoption.

Sustainable EnergySense: a predictive machine learning framework for optimizing residential electricity consumption

In a world where electricity is often taken for granted, the surge in consumption poses significant challenges, including elevated CO2 emissions and rising prices. These issues not only impact consumers but also have broader implications for the global environment. This paper endeavors to propose a smart application dedicated to optimizing the electricity consumption of household appliances. It employs Augmented Reality (AR) technology along with YOLO to detect electrical appliances and provide detailed electricity consumption insights, such as displaying the appliance consumption rate and computing the total electricity consumption based on the number of hours the appliance was used. The application utilizes Linear Regression as a machine learning (ML) algorithm to develop the electricity consumption forecasting model for the next months, based on past utility bills. Linear regression is often considered one of the most computationally lightweight ML algorithms, making it suitable for smartphones. The application also offers users practical tips for optimizing their electricity consumption habits.

Experimental study on the immersion liquid cooling performance of high-power data center servers

Highly dense and integrated data centers face key challenges of realizing efficient cooling and improved energy efficiency. To overcome these challenges, this study experimentally investigated the flow and heat transfer characteristics of single-phase immersion liquid cooling (SPILC) systems. The influence of two opposite coolant flow directions on the SPILC performance was examined. Furthermore, a correlation mechanism between coolant thermal properties and SPILC performance was established, along with a control chart for regulating the working conditions of SPILC systems. The results indicate that compared to a pro-gravity flow, an anti-gravity flow scheme reduces the chip case temperature and thermal resistance by 33.8% and 55.6%, respectively, while decreasing the power usage effectiveness (PUE) by 1.4%. Using coolant with the lowest viscosity reduces the chip case temperature and thermal resistance by 9.3% and 10.5%, respectively, while decreasing the PUE by 0.4%. Moreover, the cooling water temperature has a greater impact on the performance of SPILC systems than the volume flow rate of coolants. Additionally, this paper provides control charts for the cooling water temperature and coolant flow rate to improve the PUE while ensuring the safe operation of SPILC systems, with the highest chip temperature and total electricity consumption as indicators.

Energy Management Systems’ Modeling and Optimization in Hybrid Electric Vehicles

Optimization studies for the energy management systems of hybrid electric powertrains have critical importance as an effective measure for vehicle manufacturers to reduce greenhouse gas emissions and fuel consumption due to increasingly stringent emission regulations in the automotive industry, strict fuel economy legislation, continuously rising oil prices, and increasing consumer awareness of global warming and environmental pollution. In this study, firstly, the mathematical model of the powertrain and the rule-based energy management system of the vehicle with a power-split hybrid electric vehicle configuration are developed in the Matlab/Simulink environment and verified with real test data from the vehicle dynamometer for the UDDS drive cycle. In this way, a realistic virtual test platform has been developed where the simulation results of the energy management systems based on discrete dynamic programming and Pontryagin’s minimum principle optimization can be used to train the artificial neural network-based energy management algorithms for hybrid electric vehicles. The average fuel consumption in relation to the break specific fuel consumption of the internal combustion engine and the total electrical energy consumption of the battery in relation to the operating efficiency of the electrical machines, obtained by comparing the simulation results at the initial battery charging conditions of the vehicle using different driving cycles, will be analyzed and the advantages of the different energy management techniques used will be evaluated.

Environmental sustainability in hospitals: Dual analysis of electrical consumption and pollutant emissions

The emission of pollutants following electricity consumption is significant, and hospitals are major contributors to energy consumption. Hence, the aim of this study was a survey of electrical consumption rate and environmental pollutants emissions in selected hospitals. This descriptive-analytical and cross-sectional study was conducted over three years and based on electricity bills and bed indicators in selected hospitals in Tehran, Iran. Emissions Calculator software was used to calculate the emission of pollutants caused by electricity consumption. Kolmogorov Smirnov, Least Significant Difference (LSD), and one-way ANOVA tests were used to analyze the data. Results showed that the total average electricity consumption for each active day bed and occupied day bed was 9.6 and 13.6 times higher than the recommended standards. The average electricity consumption for each active day bed in hospitals A, B, and C was 13.7, 10.2, and 5 times higher than the standard value, respectively, while for occupied day beds, the consumption was 17.3, 15.1, and 8.5 times higher than the standard value, respectively. Additionally, the total electricity consumption in these hospitals resulted in the emission of 41837100 kg of CO2, 124815 kg of SO2, 103699 kg of NOx, 9769 kg of CO, 4885 kg of PM10, and 757 kg of VOCs into the environment. Based on the monthly active bed index and occupied day bed, there was a significant difference in the average electrical consumption among A, B, and C hospitals (p = 0.0001). Electricity consumption in the studied hospitals exceeds standards. Implementing optimal designs for lighting, heating, and cooling, along with energy optimization training, technical audits, and inspections are essential for both economic benefits and reducing environmental pollutants.

Development pathway and influencing factors of hydrogen energy storage accommodating renewable energy growth

Hydrogen energy is considered an important energy storage mode with medium- and long-term cross-seasonal storage capabilities in scenarios with high penetration of renewable energy (RE). However, there is a lack of research regarding the appropriate scale of hydrogen energy storage (HES) considering different RE power generation scenarios. In response to this gap, the present study aims to establish a power system simulation model to analyze the development pathway of HES while accommodating RE growth with a minimum cost constraint for a power system operation. The impacts of carbon trading prices, advances in HES technology, and RE resource endowment on HES development are also investigated. The findings indicate that the total available solar energy resources in China are 46.94TW, and the total available wind energy resources are 9.52TW. When the share of RE generation reaches 44.33–54.78 % of total power generation, the utilization of HES for balancing power system is advantageous in reducing the overall system costs. Looking ahead to 2060, the year when China is projected to achieve its carbon-neutrality goal, RE is estimated to reach 63.12–91.1 % of total power generation. Consequently, the HES in each province is estimated to contribute to 3.58–18.02 % of total electricity consumption. Provinces with more abundant wind resources relative to solar resources are expected to have a 36.43 % higher demand for HES.

A peer-to-peer trading model to enhance resilience: A blockchain-based smart grids with machine learning analysis towards sustainable development goals

Blockchain technology, with its peer-to-peer trading feature, influences the management of energy consumption by offering the potential to transform transparency, efficiency, and sustainability within the energy sector. Nonetheless, there is a need to develop analytical decision-making models tailored for managing peer-to-peer energy transactions to improve energy resilience. Therefore, the purpose of this paper is to address the research question: How can energy distribution systems be protected via blockchain technology to enhance energy resilience and mitigate vulnerabilities to disruptions? This paper employs a conceptual research model design and a mathematical decision-making model to address the research question by capturing the peer-to-peer trading capability of blockchain technology. The theory of planned behavior provides theoretical explanations for the proposed model. The sample includes longitudinal energy consumption data from 2015 to 2023 in Texas. The findings indicate a significant improvement in energy efficiency along with a considerable decrease in total electricity consumption. Post hoc analysis results reveal that the seasonal autoregressive integrated moving average algorithm is effective as a reliable input for the proposed mathematical model. The significant implications are to implement blockchain-based smart grids in which energy systems become more resilient to disruptions, as the peer-to-peer capability enables users to trade energy. The proposed model suggests that energy will be used more efficiently and effectively. This paper contributes to prior works by introducing a mathematical model that captures the trading behavior of energy consumers. Moreover, this paper proposes the SARIMA algorithm to predict energy demand.

Innovative retrofitting approaches for energy saving in Saudi public schools

Saudi buildings use approximately 29% of the primary energy and about 80% of the electrical energy. Because of the hot and dry climate, air conditioners consume a lot of electrical energy to attain a comfortable temperature indoors. More than 70% of Saudi buildings need to be thermally insulated. The objective of this study is to assess the existing energy saving of public schools in Saudi Arabia. Indoor thermal environment monitoring of three school buildings has been undertaken by employing LSI LASTEM ‘R-LOG’ data loggers, and electric energy bills of the schools were also collected. The school building was modelled using DesignBuilder software, which was utilized to investigate the impact of various modifications made to the building envelopes, including changes to building orientation, the inclusion of thermal insulation layers, various types of glass and shading devices. According to the findings, implementing appropriate building envelope design techniques for existing schools can lead to significant reductions in cooling and overall energy consumption, with savings of 30% and 19%, respectively. After applying these strategies collectively, the school managed to reduce its total annual electricity consumption by nearly 19.2%, resulting in a decrease from 279.13 MWh to 225.5 MWh per year.

An integrated Energy-Yield-Cost model to evaluate clean energy solutions for vertical farms

Despite the efficiency of vertical farms in terms of land and water, the main challenge of these systems is energy consumption and the cost of unit production. The aim of this study is to develop an integrated dynamic energy-yield model to determine the interactions between energy and yield and provide a cost-benefit analysis for studying different clean energy solutions. For this aim, firstly, the energy and mass balances in the case of a typical vertical farm container in Tehran, Iran are analyzed considering the plant dynamic growth and evapotranspiration model of lettuce (Lactuca sativa L.). Secondly, a cost model is added to calculate the cost of unit production. Two strategies are examined, including (wasted) CO2 injection and the implementation of the solar photovoltaic system. According to the results, the specific electricity consumption for lettuce production and the cost of unit production in the case study (a typical vertical farm container in Tehran, Iran) are 11.34 kWh/kg and 3.78 $/kg, respectively. Furthermore, the share of the lighting system and dehumidifier system in the total specific electricity consumption for lettuce production is 78 % and 19 %, respectively. Based on the sensitivity analysis, increasing photosynthetic photon flux density without adjusting CO2 concentration causes an increase in the specific electricity consumption and cost of unit production (from 4.03 $/kg at 300 µmol/(m2.s) and 400 ppm to 3.30 $/kg at 500 µmol/(m2.s) and 1000 ppm). In addition, by using wasted CO2, the cost of unit production drops by 22 % compared to the case study. The results of the solution evaluation indicate that for our case study, 4.7 m2 of solar panels are required to supply the electricity demand for each square meter of the vertical farm (2.35 m2 of solar panels are required for each square meter of cultivation area). The developed model in this research provides the possibility of examining the interaction of energy, yield, and cost of unit production with hourly time steps in a vertical farm simultaneously. Also, it can evaluate the different clean energy solutions on the vertical farms’ energy, yield, and cost of unit production.

Optimising building envelope and insulation materials for energy efficiency and life-cycle cost in Cypriot residences

ABSTRACT Residential buildings consume approximately 36% of total electricity consumption in Cyprus and this is expected to continue increasing in the coming years. This study explores the potential for energy savings in residential buildings in Cyprus through optimising building envelopes and insulation materials. Using building information modelling and energy modelling software, the study analyses 120 combinations of building envelopes and eight insulation materials, identifying an optimal combination of wall, roof, and flooring materials that could result in 32% energy load reductions and 27% lower life-cycle costs compared to the median scenario. High-density rock wool insulation on roofs and double-glazed windows were also found to offer both monetary and energy performance benefits. A sensitivity analysis revealed that the current high energy prices could provide greater economic incentives to adopt these optimisation strategies. Optimising building materials in Cyprus could play a critical role in climate strategy while also offering significant economic benefits.

Study of water-side free cooling technology for power lithium battery plant

Power lithium battery production process needs to consume a lot of energy. In this paper, an energy consumption analysis of a representative company was conducted, the energy consumption of the facilities that provide support for the process is higher than the energy consumption of the process equipment itself, which greatly affects the cost of battery manufacturing. Air-conditioning system as the most important energy-consuming system of the facilities, accounting for 25% to 45% of the total electricity consumption. The energy consumption of the cooling source system dominates, even in winter its energy consumption can reach 14% of the total energy consumption. On this basis, this paper analyzes the chilled-water-use characteristics of the air-conditioning terminal in winter through theoretical analysis, the chilled water temperature is therefore optimized according to the real-time outdoor humidity. The chilled water temperature is linearly increased after the humidity ratio fall below 7.5g/kg(a), reaching a maximum value of 12.1°C at 6g/kg(a). In addition, the cooling tower free cooling technology based on the optimized chilled water temperature is innovatively proposed to fully explore the energy-saving potential of the technology to achieve the energy saving of the cooling source system. TRNSYS simulation results show that after optimizing the chilled water temperature, the time period for cooling tower individual cooling under winter operating condition is extended by 60%, and cooling source system energy saving rate can reach 49.0% and the cost saving rate 48.0%, both of which are higher than 15% compared to the un-optimized chilled water temperature.

A study of home energy management considering carbon quota

The household energy management system (HEMS) has become an important system for energy conservation and emission reduction. In this study, home energy management considering carbon quota has been established. Firstly, the household photovoltaic output model, load model of various electrical appliances, battery load model, and charging and discharging of electric vehicles (EVs) model are established. Secondly, the carbon emission and carbon quota of household appliances and EVs are considered in these models. Thirdly, the energy optimization model of minimum the household user’s total comprehensive operation cost with the minimum total electricity consumption, carbon trading cost, battery degradation cost, and carbon quota income are proposed, taking into account constraints such as the comfort of users’ energy use time. Subsequently, the improved particle swarm optimization (IPSO) algorithm is used to tackle the problem. Compared to the standard particle swarm optimization (PSO), the IPSO has significantly improved the optimization effect. By comparing the optimization results in different scenarios, the effectiveness of the strategy is verified, and the influence of different carbon trading prices on optimal energy scheduling has been analyzed. The result shows that the comprehensive consideration of carbon trading cost and total electricity cost can reduce the household carbon emissions and the total electricity cost of the household user. By increasing the carbon trading price, the user’s carbon trading income and the EV carbon quota income increase, and the overall operating cost decreases; the guidance and regulation of carbon trading price can make a valuable contribution to HEMS optimization. Compared to the original situation, the household carbon emissions are reduced by 14.58 kg, a decrease of over 21.47%, while the total comprehensive operation cost are reduced by 14.12%. Carbon quota trading can guide household users to use electricity reasonably, reducing household carbon emissions and the total cost of household electricity.

Domestic Applicability of Solid-State Cooling

The global surge in energy consumption and the consequential environmental challenges have spurred an escalating demand for innovative, eco-friendly alternatives to current modern technology, and refrigeration systems are no different. With most households today owning a refrigerator amongst other appliances, there is a constant need for energy that is only growing by the year, in 2013 it was found that there was roughly 1.4 billion cold appliances in use within household globally, causing 450 million tons of CO2, using approximately 650 TWh, which is 1.2 times the total electricity consumption of Germany that same year, with a 27% increase to the number of cold appliances, [1]. Even with the efficiency of technology being improved over the years, there is still the ever-increasing demand for energy. Due to their complex crystal structures and the delicate balance required between their attributes, challenges do occur when attempting to identify suitable multiferroic materials, ongoing research aims to optimize their properties. Though harnessing the potential of these multiferroic materials and their intrinsic characteristics, may offer a sustainable solution for both industrial and residential refrigeration.

THE EFFECT OF WIND ENERGY ON CARBON EMISSIONS

The advances in economies require the need to consume more energy. Non-renewable energy resources produce high amount of carbon emissions. With global warning concerns, reducing greenhouse gas emissions is priority. The research question is how effective wind energy is for reducing carbon emissions. This study includes a panel data analysis to test the effect of wind energy on carbon emissions. The variables used in this article are installed capacity of wind energy production, the ratio of wind energy consumption to primary energy consumption, and the ratio of wind energy production to total electricity consumption, real GDP and total population. According to the results, the increase in the ratio of wind energy consumption to primary energy consumption reduces carbon emissions. There are 33 countries available in this research which are divided into two categories; countries with higher than average and countries with lower than average. The effect is more significant for countries with higher than average carbon emissions. GDP is significant for carbon emissions but population is not significant according to the results. According to short run results, Installed capacity of wind energy production is not statistically significant. Coefficient estimation for the ratio of wind energy production to total electricity consumption is negative and statistically significant for all countries. The ratio of wind energy consumption to primary energy consumption reduces carbon emissions. The results are consistent with the literature. More incentives for renewable energy and wind energy in particular, would be beneficial for environmentally and economically sound investments.