Energy Efficiency Modeling of Integrated Energy System in Coastal Areas

Yang, Chao; Gao, Fengkai; Dong, Minghao

doi:10.2112/si103-207.1

Cited by 90 publications

(23 citation statements)

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“…People as significant contributors to energy consumption can influence energy policy by sharing their opinions and views regarding the unfair distribution of renewable energy development burdens and benefits (Oluoch et al 2020 ). Successful energy and GHG mitigation policies entail inclusive people input to improve their institutional and regulatory environment (Omenge et al 2019 ; Yang et al 2020 ). Therefore, studies on people perceptions and preferences of renewable energy help policy-makers to design sustainable renewable energy policies (Oluoch et al 2020 )…”

Section: Introductionmentioning

confidence: 99%

Perceptions of GHG emissions and renewable energy sources in Europe, Australia and the USA

Zhang

Abbas

Iqbal

2021

Environ Sci Pollut Res

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People’s sentiments and perceptions of greenhouse gas emission and renewable energy are important information to understand their reaction to the planned mitigation policy. Therefore, this research analyzes people’s perceptions of greenhouse gas emissions and their preferences for renewable energy resources using a sample of Twitter data. We first identify themes of discussion using semantic text similarity and network analysis. Next, we measure people’s interest in renewable energy resources based on the mentioned rate in Twitter and search interest in Google trends. Then, we measure people’s sentiment toward these resources and compare the interest with sentiments to identify opportunities for policy improvement. The results indicate a minor influence of governmental assemblies on Twitter discourses compared to a very high influence of two renewable energy providers amounts to more than 40% of the tweeting activities related to renewable energy. The search interest analysis shows a slight shift in people’s interest in favor of renewable energy. The interest in geothermal energy is decreasing while interest in biomass energy is increasing. The sentiment analysis shows that biomass energy has the highest positive sentiments while solar and wind energy have higher interest. Solar and wind energy are found to be the two most promising sources for the future energy transition. Our study implies that governments should practice a higher influence on promoting awareness of the environment and converging between people’s interests and feasible energy solutions. We also advocate Twitter as a source for collecting real-time data about social preferences for environmental policy input.

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Section: Introductionmentioning

confidence: 99%

Perceptions of GHG emissions and renewable energy sources in Europe, Australia and the USA

Zhang

Abbas

Iqbal

2021

Environ Sci Pollut Res

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“…In contrast, positive radiation makes the Earth's surface warm [ 7 , 8 ]. Global warming is an indisputable fact.…”

Section: Introductionmentioning

confidence: 99%

[Retracted] Analysis of Influencing Factors of PM2.5 Concentration and Design of a Pollutant Diffusion Model Based on an Artificial Neural Network in the Environment of the Internet of Vehicles

Pan

2021

Computational Intelligence and Neuroscience

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With the development of the automobile industry, artificial intelligence, big data, 5G, and other technologies, the Internet of Vehicles (IoV) industry has entered a stage of rapid development. In this paper, a pollutant diffusion model based on an artificial neural network is designed in the context of a vehicle network. The application of artificial neural networks in haze prediction is studied. This paper first analyzes the causes and influencing factors of haze and selects the most representative and relatively large meteorological factors from temperature, wind, relative humidity, and several pollutant factors. Through training and simulation, a haze prediction model in the Beijing, Tianjin, and Hebei regions of China is established. Finally, according to the collected meteorological data, the pollutant diffusion model is established. The model is deduced by a standard mathematical formula, which makes the prediction results more accurate and rigorous, and the main conclusions and feasible scientific suggestions are obtained. The simulation results show that the method is effective. By strengthening the service system of the IoV, meteorological services can be more intelligent, and the information acquisition and service ability of the vehicle network can be effectively improved.

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“…Buildings, vehicles, and industry are the three primary energy consumption sectors globally [1][2][3]. Among those, buildings are consuming a considerable share, so that it is anticipated to reach over 30 % by 2040 [4].…”

Section: Introductionmentioning

confidence: 99%

Suggesting a Stochastic Fractal Search Paradigm in Combination With Artificial Neural Network for Early Prediction of Cooling Load in Residential Buildings

Moayedi¹,

Mosavi²

2021

Preprint

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Early prediction of thermal loads plays an essential role in analyzing energy-efficient buildings' energy performance. On the other hand, stochastic algorithms have recently shown high proficiency in dealing with this issue. These are the reasons that this work is dedicated to evaluating an innovative hybrid method for predicting the cooling load (CL) in buildings with residential usage. The proposed model is a combination of artificial neural networks and stochastic fractal search (SFS-ANN). Two benchmark algorithms, namely the grasshopper optimization algorithm (GOA) and firefly algorithm (FA), are also considered to be compared with the SFS. The non-linear effect of eight independent factors on the CL is analyzed using each model's optimal structure. Evaluation of the results outlined that all three metaheuristic algorithms (with more than 90 % correlation) can adequately optimize the ANN. In this regard, this tool's prediction error declined by nearly 23, 18, and 36 % by applying the GOA, FA, and SFS techniques. Also, all used accuracy criteria indicated the superiority of the SFS over the benchmark schemes. Therefore, it is inferred that utilizing the SFS along with ANN provides a reliable hybrid model for the early prediction of CL.

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Energy Efficiency Modeling of Integrated Energy System in Coastal Areas

Cited by 90 publications

References 0 publications

Perceptions of GHG emissions and renewable energy sources in Europe, Australia and the USA

Perceptions of GHG emissions and renewable energy sources in Europe, Australia and the USA

[Retracted] Analysis of Influencing Factors of PM2.5 Concentration and Design of a Pollutant Diffusion Model Based on an Artificial Neural Network in the Environment of the Internet of Vehicles

Suggesting a Stochastic Fractal Search Paradigm in Combination With Artificial Neural Network for Early Prediction of Cooling Load in Residential Buildings

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