Evaluating the global potential of aquifer thermal energy storage and determining the potential worldwide hotspots driven by socio-economic, geo-hydrologic and climatic conditions

Lu, Hongwei; Tian, Peipei; He, Li

doi:10.1016/j.rser.2019.06.013

Cited by 130 publications

(41 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…BBO was first developed by Simon [54]. Later it was expanded by other researchers and more particularly by Mirjalili et al [55], where the initial algorithm was enhanced and combined with MLP structure and became BBO-MLP. Due to its excellent predictivity, many scholars recommended it to be used in many complex engineering problems [122,123].…”

Section: Biogeography-based Optimizationmentioning

confidence: 99%

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

Moayedi

Mosavi

2021

Energies

View full text Add to dashboard Cite

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)).

show abstract

Section: Biogeography-based Optimizationmentioning

confidence: 99%

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

Moayedi

Mosavi

2021

Energies

View full text Add to dashboard Cite

show abstract

“…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

View full text Add to dashboard Cite

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.

show abstract

“…In fact, in such a context, the research and development of the conversion and storage of waste heat are essential to overcome the energy mismatch between supply and demand. Furthermore, this contributes to decarbonization’s global purpose, to safeguard our planet’s health and contain disastrous climate changes [ 4 , 5 ]. Indeed, TES systems integrated into industrial processes and domestic users can face energy efficiency issues, improve the use of renewable resources, and reduce greenhouse gas emissions [ 6 ].…”

Section: Introductionmentioning

confidence: 99%

Tuning Mg(OH)2 Structural, Physical, and Morphological Characteristics for Its Optimal Behavior in a Thermochemical Heat-Storage Application

et al. 2021

View full text Add to dashboard Cite

Thermochemical materials (TCM) are among the most promising systems to store high energy density for long-term energy storage. To be eligible as candidates, the materials have to fit many criteria such as complete reversibility of the reaction and cycling stability, high availability of the material at low cost, environmentally friendliness, and non-toxicity. Among the most promising TCM, the Mg(OH)2/MgO system appears worthy of attention for its properties in line with those required. In the last few decades, research focused its attention on the optimization of attractive hydroxide performance to achieve a better thermochemical response, however, often negatively affecting its energy density per unit of volume and therefore compromising its applicability on an industrial scale. In this study, pure Mg(OH)2 was developed using different synthesis procedures. Reverse deposition precipitation and deposition precipitation methods were used to obtain the investigated samples. By adding a cationic surfactant (cetyl trimethylammonium bromide), deposition precipitation Mg(OH)2 (CTAB-DP-MH) or changing the precipitating precursor (N-DP-MH), the structural, physical and morphological characteristics were tuned, and the results were compared with a commercial Mg(OH)2 sample. We identified a correlation between the TCM properties and the thermochemical behavior. In such a context, it was demonstrated that both CTAB-DP-MH and N-DP-MH improved the thermochemical performances of the storage medium concerning conversion (64 wt.% and 74 wt.% respectively) and stored and released heat (887 and 1041 kJ/kgMg(OH)2). In particular, using the innovative technique not yet investigated for thermal energy storage (TES) materials, with NaOH as precipitating precursor, N-DP-MH reached the highest stored and released heat capacity per volume unit, ~684 MJ/m3.

show abstract

Evaluating the global potential of aquifer thermal energy storage and determining the potential worldwide hotspots driven by socio-economic, geo-hydrologic and climatic conditions

Cited by 130 publications

References 26 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

Tuning Mg(OH)2 Structural, Physical, and Morphological Characteristics for Its Optimal Behavior in a Thermochemical Heat-Storage Application

Contact Info

Product

Resources

About