A novel long term solar photovoltaic power forecasting approach using LSTM with Nadam optimizer: A case study of India

Sharma, Jatin; Soni, Sameer; Paliwal, Priyanka; Shaik, Saboor; Chaurasiya, Prem Kumar; Sharifpur, Mohsen; Khalilpoor, Nima; Afzal, Asif

doi:10.1002/ese3.1178

Cited by 57 publications

(37 citation statements)

References 48 publications

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“…From the result it is revealed that the COF of GFRPA66 with 35 wt.% is low as compared with GFRPA66 with 30 wt.% reinforcements, since it has better transfer layer formation, increased adhesion of PA66, and low abrasion by glass fiber with less temperature between the contact surfaces. Also the elastic modulus and ultimate strength of glass fiber improve as the weight of glass fiber increases [ 70 , 71 , 72 , 73 ].…”

Section: Resultsmentioning

confidence: 99%

Response Surface Methodology Based Optimization of Test Parameter in Glass Fiber Reinforced Polyamide 66 for Dry Sliding, Tribological Performance

Jagadeesan¹,

Selvaraj

Santhosh

et al. 2022

Materials

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The tribological performance of a glass fiber reinforced polyamide66 (GFRPA66) composite with varying fiber weight percentage (wt.%) [30 wt.% and 35 wt.%] is investigated in this study using a pin-on-disc tribometer. GFRPA66 composite specimens in the form of pins with varying percentages of fiber viz., 30 wt.% and 35 wt.% are fabricated by an injection molding process. Tribological performances, such as coefficient of friction (COF) and the specific wear rate (SWR), are investigated. The factors affecting the wear of GFRPA66 composites [with 30 wt.% and 35 wt.% reinforcements] are identified based on the process parameters such as load, sliding velocity, and sliding distance. Design Expert 13.0 software is used for the experimental data analysis, based on the design of experiments planned in accordance with the central composite design (CCD) of the response surface methodology (RSM) technique. The significance of the obtained results are analyzed using analysis of variance (ANOVA) techniques. To attain minimum SWR and COF, the wear performance is optimized in dry sliding conditions. The analysis of experimental data revealed that SWR and COF increased with increasing load, sliding velocity, and sliding distance for GFRPA66 [30 wt.%], but decreased with increasing polyamide weight percentage. The SWR for a maximum load of 80 N, and for a sliding velocity of 0.22 m/s, and a sliding distance of 3500 m for GFRPA66 composite specimens with 30 wt.% reinforcements are found to be 0.0121 m3/Nm, while the SWR for the same set of parameters for GFRPA66 composite specimens with 35 wt.% reinforcements are found to be 0.0102 m3/Nm. The COF for the GFRPA66 composite specimens with 30 wt.% reinforcements for the above set of parameters is found to be 0.37, while the GFRPA66 composite specimens with 35 wt.% reinforcements showed significant improvement in wear performance with a reduction in COF to 0.25. Finally, using a scanning electron microscope (SEM), the worn surfaces of the GFRPA66 are examined and interpreted.

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Section: Resultsmentioning

confidence: 99%

Response Surface Methodology Based Optimization of Test Parameter in Glass Fiber Reinforced Polyamide 66 for Dry Sliding, Tribological Performance

Jagadeesan¹,

Selvaraj

Santhosh

et al. 2022

Materials

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“…This could result from fuel evaporation during the ignition delay interval, which reduces the temperature . During the premixed combustion stage, uncoated engines for both fuels yield a lower rate of heat generation than coated engines. , This is because a longer ignition delay permits the injection of more fuel. This is attributed to lower HRR achieved for uncoated engines .…”

Section: Resultsmentioning

confidence: 99%

Effect of Kariba Weed Biodiesel Blended with n-Pentane on the Chosen Parameters of a Ceramic-Coated Thermal Barrier Direct Injection Diesel Engine

Venkatesan

Parthasarathy

Ellappan³

et al. 2022

ACS Omega

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In this experimental investigation, Kariba weed biodiesel (KSB) blended with n-pentane has been tested in conventional and ceramic-coated thermal barrier engines, and the results have been compiled and presented. A single-cylinder, four-stroke, direct injection diesel engine has been used as the test engine with eddy current dynamometer loading as used in the experimental setup. The tests were repeated in various ambient conditions to get an optimal value. Ceramic coating has been done with partially stabilized zirconia by the plasma arc spraying process. Among the quantum of tests conducted, 90% KSB blended with 10% n-pentane showed appreciable results when it was compared with the test fuel (neat diesel). The brake thermal efficiency and brake-specific fuel consumption were found to be better when compared with neat diesel. At increasing load, unburnt hydrocarbon, carbon monoxide, and smoke opacity emissions were appreciably reduced.

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“…Since GP and SVM-Schotastic model performed the best among the other models for FS and CS for this dataset, sensitivity analysis was carried out on it by changing the input combination and taking out one input parameter at a time, as shown in Table 9 and Table 10 . Statistical assessment metrics such as CC, MAE, and RMSE were used to assess each model’s performance [ 72 , 73 , 74 , 75 , 76 , 77 , 78 , 79 , 80 , 81 , 82 ]. Table 9 and Table 10 , demonstrates that the number of curing days followed by CA, C, w and MP is critical in predicting the flexural and compressive strength of a concrete mix.…”

Section: Sensitivity Analysismentioning

confidence: 99%

“…Table 9 and Table 10 , demonstrates that the number of curing days followed by CA, C, w and MP is critical in predicting the flexural and compressive strength of a concrete mix. Due to the pozzolanic reactions, concrete recovers 60% of its strength after 7 days of curing and increases by 99% after 28 days, resulting in a low CC value after removing the aforementioned characteristic [ 76 , 77 , 78 , 79 ]. The pozzolanic reaction is a slow process, and as the curing period lengthens, the amount of gel produced in the mix increases, resulting in greater strength [ 65 ].…”

Section: Sensitivity Analysismentioning

confidence: 99%

Machine Learning Techniques for Evaluating Concrete Strength with Waste Marble Powder

et al. 2022

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The purpose of the research is to predict the compressive and flexural strengths of the concrete mix by using waste marble powder as a partial replacement of cement and sand, based on the experimental data that was acquired from the laboratory tests. In order to accomplish the goal, the models of Support vector machines, Support vector machines with bagging and Stochastic, Linear regression, and Gaussian processes were applied to the experimental data for predicting the compressive and flexural strength of concrete. The effectiveness of models was also evaluated by using statistical criteria. Therefore, it can be inferred that the gaussian process and support vector machine methods can be used to predict the respective outputs, i.e., flexural and compressive strength. The Gaussian process and Support vector machines Stochastic predicts better outcomes for flexural and compressive strength because it has a higher coefficient of correlation (0.8235 and 0.9462), lower mean absolute and root mean squared error values as (2.2808 and 1.8104) and (2.8527 and 2.3430), respectively. Results suggest that all applied techniques are reliable for predicting the compressive and flexural strength of concrete and are able to reduce the experimental work time. In comparison to input factors for this data set, the number of curing days followed by the CA, C, FA, w, and MP is essential in predicting the flexural and compressive strength of a concrete mix for this data set.

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A novel long term solar photovoltaic power forecasting approach using LSTM with Nadam optimizer: A case study of India

Cited by 57 publications

References 48 publications

Response Surface Methodology Based Optimization of Test Parameter in Glass Fiber Reinforced Polyamide 66 for Dry Sliding, Tribological Performance

Response Surface Methodology Based Optimization of Test Parameter in Glass Fiber Reinforced Polyamide 66 for Dry Sliding, Tribological Performance

Effect of Kariba Weed Biodiesel Blended with n-Pentane on the Chosen Parameters of a Ceramic-Coated Thermal Barrier Direct Injection Diesel Engine

Machine Learning Techniques for Evaluating Concrete Strength with Waste Marble Powder

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