Hourly Forecasting of Solar Photovoltaic Power in Pakistan Using Recurrent Neural Networks

The solar photovoltaic (PV) power forecast is crucial for steady grid operation, scheduling, and grid electricity management. In this work, numerous time series forecast methodologies, including the statistical and artificial intelligence-based methods, are studied and compared fastidiously to forecast PV electricity. Moreover, the impact of different environmental conditions for all of the algorithms is investigated. Hourly solar PV power forecasting is done to confirm the effectiveness of various models. Data used in this paper is of one entire year and is acquired from a 100 MW solar power plant, namely, Quaid-e-Azam Solar Park, Bahawalpur, Pakistan. This paper suggests recurrent neural networks (RNNs) as the best-performing forecasting model for PV power output. Furthermore, the bidirectional long-short-term memory RNN framework delivered high accuracy results in all weather conditions, especially under cloudy weather conditions where root mean square error (RMSE) was found lowest 0.0025, R square stands at 0.99, and coefficient of variation of root mean square error (RMSE) Cv was observed 0.0095%.

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Subset selection of markers for the genome-enabled prediction of genetic values using radial basis function neural networks

Acta Scientiarum Agronomy ◽

10.4025/actasciagron.v43i1.46307 ◽

2020 ◽

Vol 43 ◽

pp. e46307 ◽

Cited By ~ 1

Author(s):

Isabela de Castro Sant'Anna ◽

Gabi Nunes Silva ◽

Moysés Nascimento ◽

Cosme Damião Cruz

Keyword(s):

Neural Networks ◽

Root Mean Square Error ◽

Radial Basis Function ◽

Mean Square Error ◽

Root Mean Square ◽

Basis Function ◽

Stepwise Regression ◽

Subset Selection ◽

Mean Square ◽

Selection Of

This paper aimed to evaluate the effectiveness of subset selection of markers for genome-enabled prediction of genetic values using radial basis function neural networks (RBFNN). To this end, an F1 population derived from the hybridization of divergent parents with 500 individuals genotyped with 1000 SNP-type markers was simulated. Phenotypic traits were determined by adopting three different gene action models – additive, additive-dominant, and epistatic, representing two dominance situations: partial and complete with quantitative traits having a heritability (h2) of 30 and 60%; traits were controlled by 50 loci, considering two alleles per locus. Twelve different scenarios were represented in the simulation. The stepwise regression was used before the prediction methods. The reliability and the root mean square error were used for estimation using a fivefold cross-validation scheme. Overall, dimensionality reduction improved the reliability values for all scenarios, specifically with h2 =30 the reliability value from 0.03 to 0.59 using RBFNN and from 0.10 to 0.57 with RR-BLUP in the scenario with additive effects. In the additive dominant scenario, the reliability values changed from 0.12 to 0.59 using RBFNN and from 0.12 to 0.58 with RR-BLUP, and in the epistasis scenarios, the reliability values changed from 0.07 to 0.50 using RBFNN and from 0.06 to 0.47 with RR-BLUP. The results showed that the use of stepwise regression before the use of these techniques led to an improvement in the accuracy of prediction of the genetic value and, mainly, to a large reduction of the root mean square error in addition to facilitating processing and analysis time due to a reduction in dimensionality.

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Penerapan Generalized Regression Neural Networks untuk Memprediksi Produksi Padi Terhadap Perubahan Iklim

Jurnal Teknologi Rekayasa ◽

10.31544/jtera.v2.i2.2017.117-124 ◽

2017 ◽

Vol 2 (2) ◽

pp. 117 ◽

Cited By ~ 1

Author(s):

Muhammad Alkaff ◽

Yuslena Sari

Keyword(s):

Neural Networks ◽

Root Mean Square Error ◽

Mean Square Error ◽

Root Mean Square ◽

Input Data ◽

Mean Square ◽

Generalized Regression Neural Networks ◽

Data Output ◽

Generalized Regression

Padi sebagai bahan makanan pokok utama bagi masyarakat Indonesia merupakan tanaman pangan yang rentan terhadap perubahan iklim. Pendataan dan perhitungan ramalan hasil produksi padi sangat diperlukan untuk mendukung kebijakan yang berkaitan dengan ketahanan pangan. Penelitian ini bertujuan untuk melakukan peramalan terhadap produksi padi di Kabupaten Barito Kuala sebagai kabupaten penghasil padi terbesar di Kalimantan Selatan dengan menggunakan data iklim sebagai input. Data iklim yang digunakan berasal dari Stasiun Meteorologi Syamsudin Noor, sedangkan sebagai data output adalah data produksi padi dari Badan Pusat Statistika (BPS) Provinsi Kalimantan Selatan. Metode yang digunakan untuk melakukan peramalan produksi padi adalah Generalized Regression Neural Networks (GRNN). Dari hasil pengujian didapatkan nilai Root Mean Square Error (RMSE) sebesar 0,296 dengan menggunakan parameter smoothness bernilai 1.Kata kunci: padi, iklim, Barito Kuala, GRNN, RMSE

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Deep Learning Stream Temperature Model: Recommendations for Modeling Gauged and Ungauged Basins

10.5194/egusphere-egu21-13960 ◽

2021 ◽

Author(s):

Farshid Rahmani ◽

Kathryn Lawson ◽

Samantha Oliver ◽

Alison Appling ◽

Chaopeng Shen

Keyword(s):

Deep Learning ◽

Root Mean Square Error ◽

Mean Square Error ◽

Root Mean Square ◽

The United States ◽

Data Availability ◽

Efficiency Coefficient ◽

Mean Square ◽

Training Set ◽

The Impact

Stream water temperature (Ts) is a variable that plays a pivotal role in managing water resources. We used the long short-term memory (LSTM) deep learning architecture to develop a basin centric single Ts model based on general meteorological data and basin meteo-geological attributes. We created a strong tool for long-term Ts projection and subsequently, improved the Ts model using novel approaches. We investigated the impact of both observed and simulated streamflow data on improving the model accuracy. At a national scale, we obtained a median root-mean-square error (RMSE) of 0.69 oC, and Nash-Sutcliffe model efficiency coefficient (NSE) of 0.985, which are marked improvements over previous values reported in previous studies. In order to test the performance of the model on basins ranging from basins with extensive data to unmonitored basins, we used more than 400 basins with different data-availability groups (DAG) across the continent of the United States to explore how to assemble the training dataset for both monitored and unmonitored basins. Best root-mean-square error (RMSE) for sites with extensive (99%), intermediate (60%), scarce (10%) and absent (0%) data for training were 0.75, 0.837, 0.889, and 1.595 oC, respectively. We observed the negative effect of the presence of reservoirs in Ts modeling. Our results illustrated that the most suitable training set should be different in modeling basins with different availability of observed data. for predicting Ts in a monitored basin, including basins that have at least equal DAG with that particular basin will result in most accurate predictions, however, for Ts prediction in ungauged basin, including all basins in training section will generate the best model, showing a more diverse training set. Furthermore, to decrease overfitting produced by attributes for PUB application, we could improve the accuracy of the model using input-selection ensemble method. We got median correlation higher than 0.90 for PUB after seasonality was removed which is still high. While many Ts prediction models showed better performance in summer, our model was on the opposite side. We found a strong relationship between general available daily meteorological variables and catchment attributes with the presented Ts model. However, our results indicate that combining physics-based criteria to the model can improve the prediction of temperature in river networks..

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Hydrochars as Emerging Biofuels: Recent Advances and Application of Artificial Neural Networks for the Prediction of Heating Values

Energies ◽

10.3390/en13174572 ◽

2020 ◽

Vol 13 (17) ◽

pp. 4572

Author(s):

Ioannis O. Vardiambasis ◽

Theodoros N. Kapetanakis ◽

Christos D. Nikolopoulos ◽

Trinh Kieu Trang ◽

Toshiki Tsubota ◽

...

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Sewage Sludge ◽

Root Mean Square Error ◽

Mean Square Error ◽

Root Mean Square ◽

Food Waste ◽

Hydrothermal Carbonization ◽

Mean Square ◽

Artificial Neural

In this study, the growing scientific field of alternative biofuels was examined, with respect to hydrochars produced from renewable biomasses. Hydrochars are the solid products of hydrothermal carbonization (HTC) and their properties depend on the initial biomass and the temperature and duration of treatment. The basic (Scopus) and advanced (Citespace) analysis of literature showed that this is a dynamic research area, with several sub-fields of intense activity. The focus of researchers on sewage sludge and food waste as hydrochar precursors was highlighted and reviewed. It was established that hydrochars have improved behavior as fuels compared to these feedstocks. Food waste can be particularly useful in co-hydrothermal carbonization with ash-rich materials. In the case of sewage sludge, simultaneous P recovery from the HTC wastewater may add more value to the process. For both feedstocks, results from large-scale HTC are practically non-existent. Following the review, related data from the years 2014–2020 were retrieved and fitted into four different artificial neural networks (ANNs). Based on the elemental content, HTC temperature and time (as inputs), the higher heating values (HHVs) and yields (as outputs) could be successfully predicted, regardless of original biomass used for hydrochar production. ANN3 (based on C, O, H content, and HTC temperature) showed the optimum HHV predicting performance (R2 0.917, root mean square error 1.124), however, hydrochars’ HHVs could also be satisfactorily predicted by the C content alone (ANN1, R2 0.897, root mean square error 1.289).

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Comparison of neural networks and genetic algorithms to determine missing precipitation data (Case study: the city of Sari)

Revista de la Universidad del Zulia ◽

10.46925//rdluz.29.08 ◽

2020 ◽

Vol 11 (29) ◽

pp. 114-128

Author(s):

Ali Mahdavi ◽

Mohsen Najarchi ◽

Emadoddin Hazaveie ◽

Seyed Mohammad Mirhosayni Hazave ◽

Seyed Mohammad Mahdai Najafizadeh

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Genetic Programming ◽

Root Mean Square Error ◽

Mean Square Error ◽

Root Mean Square ◽

Correlation Coefficients ◽

Mean Square ◽

Artificial Neural ◽

The City

Neural networks and genetic programming in the investigation of new methods for predicting rainfall in the catchment area of the city of Sari. Various methods are used for prediction, such as the time series model, artificial neural networks, fuzzy logic, fuzzy Nero, and genetic programming. Results based on statistical indicators of root mean square error and correlation coefficient were studied. The results of the optimal model of genetic programming were compared, the correlation coefficients and the root mean square error 0.973 and 0.034 respectively for training, and 0.964 and 0.057 respectively for the optimal neural network model. Genetic programming has been more accurate than artificial neural networks and is recommended as a good way to accurately predict.

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A Study on Time Series Forecasting using Hybridization of Time Series Models and Neural Networks

Recent Advances in Computer Science and Communications ◽

10.2174/1573401315666190619112842 ◽

2020 ◽

Vol 13 (5) ◽

pp. 827-832

Author(s):

Iflah Aijaz ◽

Parul Agarwal

Keyword(s):

Machine Learning ◽

Neural Networks ◽

Time Series ◽

Root Mean Square Error ◽

Mean Square Error ◽

Root Mean Square ◽

Hybrid Models ◽

Time Series Forecasting ◽

Percentage Error ◽

Mean Square

Introduction: Auto-Regressive Integrated Moving Average (ARIMA) and Artificial Neural Networks (ANN) are leading linear and non-linear models in Machine learning respectively for time series forecasting. Objective: This survey paper presents a review of recent advances in the area of Machine Learning techniques and artificial intelligence used for forecasting different events. Methods: This paper presents an extensive survey of work done in the field of Machine Learning where hybrid models for are compared to the basic models for forecasting on the basis of error parameters like Mean Absolute Deviation (MAD), Mean Square Error (MSE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and Normalized Root Mean Square Error (NRMSE). Results: Table 1 summarizes important papers discussed in this paper on the basis of some parameters which explain the efficiency of hybrid models or when the model is used in isolation. Conclusion: The hybrid model has realized accurate results as compared when the models were used in isolation yet some research papers argue that hybrids cannot always outperform individual models.

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Reliability and accuracy of helmet-mounted and head-mounted devices used to measure head accelerations

Proceedings of the Institution of Mechanical Engineers Part P Journal of Sports Engineering and Technology ◽

10.1177/1754337116658395 ◽

2016 ◽

Vol 231 (2) ◽

pp. 144-153 ◽

Cited By ~ 9

Author(s):

Brian Cummiskey ◽

David Schiffmiller ◽

Thomas M Talavage ◽

Larry Leverenz ◽

Janette J Meyer ◽

...

Keyword(s):

Root Mean Square Error ◽

Mean Square Error ◽

Root Mean Square ◽

Mean Square ◽

High School Football ◽

Head Impacts ◽

Football Team ◽

Hybrid Iii ◽

Future Work ◽

The Impact

The attention given to brain injury has grown in recent years as its effects have become better understood. A desire to investigate the causal agents of head trauma in athletes has led to the development and use of several devices that track head impacts. In order to determine which devices best measure these impacts, a Hybrid III headform was used to quantify the accuracy for translational and angular accelerations. Testing was performed by mounting each device into the helmet as instructed by its manufacturer, fitting the helmet on the headform, and impacting the helmet using an impulse hammer. The root mean square error for the peak translational acceleration varied with location. The worst root mean square error for a head-mounted device was 74.7% while the worst for a helmet-mounted device was 298%. Head-mounted devices consistently outperformed those mounted in helmets, suggesting that future sensor designs should avoid attachment to the helmet. Deployment to a high school football team affirmed differences between two of the device models, but strongly indicated that head-mounted systems require further development to account for variation between individuals, the relative motion of the skin, and helmet–sensor interactions. Future work needs to account for these issues, refine the algorithms used to estimate the translational and angular accelerations, and examine technologies that better locate the source of the impact.

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Prediction of Significant Wave Height Using Neural Network in the Java Sea (North of Surabaya)

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.862.72 ◽

2017 ◽

Vol 862 ◽

pp. 72-77

Author(s):

Wimala L. Dhanistha ◽

R.A. Atmoko ◽

P. Juniarko ◽

Ridho Akbar

Keyword(s):

Neural Network ◽

Neural Networks ◽

Root Mean Square Error ◽

Mean Square Error ◽

Root Mean Square ◽

Wave Height ◽

Mean Square ◽

The North ◽

Java Sea ◽

The City

Indonesia is an archipelago, Surabaya is the second crowded city in Indonesia. So the shipping lane and the city is comparable. Neural network is models inspired by biological neural networks and used to estimate or approximate functions that can depend on a large number of inputs and are generally unknown. Neural network is used to predict the wave height in Java Sea (The North of Surabaya). The Root Mean Square Error average for the next one hour is 0.03 and the Root Mean Square Error average for the next six hours is 0.09. That’s mean the longest the prediction, the biggest Root Mean Square error.

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Weather-Based Neural Network, Stepwise Linear and Sparse Regression Approach for Rabi Sorghum Yield Forecasting of Karnataka, India

Agronomy ◽

10.3390/agronomy10111645 ◽

2020 ◽

Vol 10 (11) ◽

pp. 1645

Author(s):

Shankarappa Sridhara ◽

Nandini Ramesh ◽

Pradeep Gopakkali ◽

Bappa Das ◽

Soumya Venkatappa ◽

...

Keyword(s):

Neural Network ◽

Root Mean Square Error ◽

Mean Square Error ◽

Root Mean Square ◽

Ridge Regression ◽

Principal Component ◽

Growth Period ◽

Weather Conditions ◽

Mean Square ◽

Yield Forecasting

Sorghum is an important dual-purpose crop of India grown for food and fodder. Prevailing weather conditions during the crop growth period determine the yield of sorghum. Hence, the crop yield forecasting models based on weather parameters will be an appropriate option for policymakers and researchers to develop sustainable cropping strategies. In the present study, six multivariate weather-based models viz., least absolute shrinkage and selection operator (LASSO), elastic net (ENET), principal component analysis (PCA) in combination with stepwise multiple linear regression (SMLR), artificial neural network (ANN) alone and in combination with PCA and ridge regression model are examined by fixing 90% of the data for calibration and remaining dataset for validation to forecast rabi sorghum yield for different districts of Karnataka. The R2 and root mean square error (RMSE) during calibration ranged between 0.42 to 0.98 and 30.48 to 304.17 kg ha−1, respectively, without actual evapotranspiration (AET) whereas, these evaluation parameters varied from 0.38 to 0.99 and 19.84 to 308.79 kg ha−1, respectively with AET inclusion. During validation, the RMSE and nRMSE (normalized root mean square error) varied between 88.99 to 1265.03 kg ha−1 and 4.49 to 96.84%, respectively without AET and including AET as one of the weather variable RMSE and nRMSE were 63.48 to 1172.01 kg ha−1 and 4.16 to 92.56%, respectively. The performance of six multivariate models revealed that LASSO was the best model followed by ENET compared to PCA_SMLR, ANN, PCA_ANN and ridge regression models because of reduced overfitting through penalisation of regression coefficient. Thus, it can be concluded that LASSO and ENET weather-based models can be effectively utilized for the district level forecast of sorghum yield.

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Prediction, modeling and characterization of surface texturing by sulfuric etchant on non-toxic titanium bio-material using artificial neural networks and fuzzy logic systems

Science and Engineering of Composite Materials ◽

10.1515/secm-2014-0230 ◽

2016 ◽

Vol 23 (4) ◽

pp. 423-433 ◽

Cited By ~ 5

Author(s):

Hossein Mohammad Khanlou ◽

Bee Chin Ang ◽

Mohsen Marani Barzani

Keyword(s):

Neural Networks ◽

Surface Roughness ◽

Sulfuric Acid ◽

Root Mean Square Error ◽

Mean Square Error ◽

Root Mean Square ◽

Acid Etching ◽

Mean Square ◽

Inference System ◽

Feed Forward Network

AbstractMultilayer feed forward network, radial biased function network, generalized regression neural network and adaptive network-based fuzzy inference system (ANFIS) were used to predict the surface roughness of Ti-13Zr-13Nb alloy in etching sulfuric acid. Subsequent processes – polishing, sandblasting and acid etching or SLA – were employed to modify the surface. Alumina particles for surface blasting and concentrated sulfuric acid for acid etching were utilized in this experiment. This was performed for three different periods of time (10, 20 and 30 s) and temperature (25, 45 and 60°C). Correspondingly, the Ti-13Zr-13Nb surfaces were evaluated using a field emission scanning electron microscope for roughening and a contact mode profilometer for the average surface roughness (Ra) (nm). Different configurations of neural networks and ANFIS approaches are examined in order to minimize the root mean square error. Consequently, the ANFIS model is selected by dividing the time and temperature into one and three spaces, respectively, using the Gaussian-shaped membership function. A mathematical model is attained from the best approach in terms of root mean square error to realize the relation of the surface roughness of Ti-13Zr-13Nb alloy in etching sulfuric acid and time and temperature as the effective parameters.

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