Game-based analysis of energy-water nexus for identifying environmental impacts during Shale gas operations under stochastic input

He, Li; Chen, Yizhong; Zhao, Haijun; Tian, Peipei; Xue, Ying; Chen, Liang

doi:10.1016/j.scitotenv.2018.02.004

Cited by 112 publications

(31 citation statements)

References 42 publications

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“…With recent advances in computational intelligence, many scholars have replaced traditional methods with new generated machine learning [6][7][8][9][10][11], deep learning [12][13][14][15][16][17], decision making [18,19], and artificial intelligence-based tools [20][21][22]. These novel approximation techniques are well employed in various engineering fields such as in evaluating environmental concerns [19,[23][24][25][26][27][28][29][30][31], implications for natural environmental management [32][33][34][35][36][37][38][39], water resources management [28,[40][41][42][43][44], natural gas consumption [45][46][47][48], energy efficiency [49][50]…”

Section: Background Of Artificial Intelligencementioning

confidence: 99%

Double-Target Based Neural Networks in Predicting Energy Consumption in Residential Buildings

Moayedi

Mosavi

2021

Energies

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A reliable prediction of sustainable energy consumption is key for designing environmentally friendly buildings. In this study, three novel hybrid intelligent methods, namely the grasshopper optimization algorithm (GOA), wind-driven optimization (WDO), and biogeography-based optimization (BBO), are employed to optimize the multitarget prediction of heating loads (HLs) and cooling loads (CLs) in the heating, ventilation and air conditioning (HVAC) systems. Concerning the optimization of the applied algorithms, a series of swarm-based iterations are performed, and the best structure is proposed for each model. The GOA, WDO, and BBO algorithms are mixed with a class of feedforward artificial neural networks (ANNs), which is called a multi-layer perceptron (MLP) to predict the HL and CL. According to the sensitivity analysis, the WDO with swarm size = 500 proposes the most-fitted ANN. The proposed WDO-ANN provided an accurate prediction in terms of heating load (training (R2 correlation = 0.977 and RMSE error = 0.183) and testing (R2 correlation = 0.973 and RMSE error = 0.190)) and yielded the best-fitted prediction in terms of cooling load (training (R2 correlation = 0.99 and RMSE error = 0.147) and testing (R2 correlation = 0.99 and RMSE error = 0.148)).

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Section: Background Of Artificial Intelligencementioning

confidence: 99%

Double-Target Based Neural Networks in Predicting Energy Consumption in Residential Buildings

Moayedi

Mosavi

2021

Energies

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“…It is well established that the cost and use of energy affect human lives every day. In this sense, many issues arise from the content of energy consumption such as acid rain, dependency on depleting supplies of fossil fuels, greenhouse gas emissions [8][9][10][11][12][13][14][15][16][17], climate change [18][19][20][21], as well as environmental concerns that come along with energy power supply [22][23][24][25][26][27][28][29][30][31][32][33][34][35]. In recent years, various techniques have been used for the optimal design of the HVAC system [36][37][38][39][40][41][42][43][44].…”

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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“…With recent advances in computational intelligence, many scholars have replaced traditional methods with economical and accurate machine learning , deep learning [1][2][3][4][5][6][7], decision making [8; 9], and artificial intelligence-based tools [10][11][12][13][14]. These novel approximation techniques are well employed in various engineering field such as in evaluating the environmental concerns [15][16][17][18][19][20][21][22][23][24][25], implications for natural environmental [26][27][28][29][30][31][32][33][34], water resources management [35][36][37][38][39][40][41], energy efficiency [42][43][44][45][46][47][48]…”

Section: Introductionmentioning

confidence: 99%

A Water Cycle-based Error Minimization Technique in Predicting Bearing Capacity of Foundation

Moayedi

Mosavi

2021

Preprint

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Selecting the appropriate training technique is a significant step in utilizing intelligent approaches. It becomes even more important when it comes to critical problems like analyzing the bearing capacity of foundations. This study investigates the feasibility of a capable metaheuristic algorithm, called water cycle algorithm (WCA), for training a multi-layer perceptron (MLP). The WCA-MLP is applied to a large finite element dataset to predict the settlement. The results of this model are compared with electromagnetic field optimization (EFO) and shuffled complex evolution (SCE) benchmarks. With reference to the obtained Pearson correlation factors (larger than 0.88 in all stages), all employed models are suitable for the mentioned objective. Moreover, it was observed that the training error of the WCA was 5.84 and 3.89% smaller than the EFO and SCE, respectively. Likewise, the accuracy of the WCA-MLP was 1.85 and 2.04% larger in the testing phase. Also, a predictive equation is finally elicited for practical applications in compatible circumstances.

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Game-based analysis of energy-water nexus for identifying environmental impacts during Shale gas operations under stochastic input

Cited by 112 publications

References 42 publications

Double-Target Based Neural Networks in Predicting Energy Consumption in Residential Buildings

Double-Target Based Neural Networks in Predicting Energy Consumption in Residential Buildings

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

A Water Cycle-based Error Minimization Technique in Predicting Bearing Capacity of Foundation

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