Analysis and forecasting of weather conditions in Oman for renewable energy applications

Yousif, Jabar H.; Al-Balushi, Haitham A.; Kazem, Hussein A.; Chaichan, Miqdam T.

doi:10.1016/j.csite.2018.11.006

Cited by 41 publications

(24 citation statements)

References 27 publications

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“…Estimation of roof-top photovoltaic solar energy is implemented using Support vector machine (SVM) and Geographic information systems (GIS). 12 The multi-boundary score is used for predicting solar energy generation 13 and solar irradiance prediction using deep neural networks. 14 Wind speed forecasting wavelet transform and support vector machines, 15 Artificial neural networks (ANN), 16 SVM, 17 random forest, 18 K-nearest neighbor (KNN), Support vector regression (SVR) 19,20 models predict wind and solar power generation using weather information.…”

Section: Renewable Energy Forecastingmentioning

confidence: 99%

A holistic review on energy forecasting using big data and deep learning models

Devaraj¹,

Elavarasan

Shafiullah

et al. 2021

Int J Energy Res

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With the growth of forecasting models, energy forecasting is used for better planning, operation, and management in the electric grid. It is important to improve the accuracy of forecasting for a faster decision-making process. Big data can handle large scale of datasets and extract the patterns fed to the deep learning models that improve the accuracy than the traditional models and hence, recently started its application in energy forecasting. In this study, an in-depth insight is initially derived by investigating artificial intelligence (AI) and machine learning (ML) techniques with their strengths and weaknesses, enhancing the consistency of renewable energy integration and modernizing the overall grid. However, Deep learning (DL) algorithms have the capability to handle big data by capturing the inherent non-linear features through automatic feature extraction methods. Hence, an extensive and exhaustive review of generative, hybrid, and discriminative DL models is being examined for short-term, medium-term, and long-term forecasting of renewable energy, energy consumption, demand, and supply etc. This study also explores the different data decomposition strategies used to build forecasting models. The recent success of DL is being investigated, and the insights of paradoxes in parameter optimization during the training of the model are identified. The impact of weather prediction in the wind and solar energy forecasting is examined in detail. From the existing literatures, it has seen that the average mean absolute percentage error (MAPE) value of solar and wind energy forecasting is 10.29% and 6.7% respectively. Current technology barriers involved in implementing these models for energy forecasting and the recommendations to overcome the existing system barriers are identified. An indepth analysis, discussions of the results, and the scope for improvement are provided in this study including the potential directions for future research in the energy forecasting.

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Section: Renewable Energy Forecastingmentioning

confidence: 99%

A holistic review on energy forecasting using big data and deep learning models

Devaraj¹,

Elavarasan

Shafiullah

et al. 2021

Int J Energy Res

View full text Add to dashboard Cite

show abstract

“…Experiments shows that it can compress the data up to 50% but data processing duration suffers from the sampling interval, data length and computation power of the device. J. H. Yousif et al [6] focused on the weather conditions and its impact over the various factors like temperature/humidity/wind velocity/rain etc. and developed a hypothesis based perdition model that is used for weather forecasting.…”

Section: II Data Logging Over Wsnmentioning

confidence: 99%

Performance Analysis of Routing Protocols over WSN under the constraints of Natural Hazards using Data Logger

Kaur¹,

Bindal²

2019

IJITEE

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“…The availability of useful data of climate parameters such as solar irradiation and ambient temperature is essential to identify and predict the total energy available for use by a PV system. 29 The IEA highlights the importance of taking age into consideration when predicting PV performance accurately. Quoted from to the actual state of the art of the performance modelling as given by the reports of IEA PVPS task 13, "Subtask 1.4 will focus on the service life prediction of PV modules.…”

Section: Introductionmentioning

confidence: 99%

“…The size and performance of the PV system, therefore, depend heavily on meteorological conditions. The availability of useful data of climate parameters such as solar irradiation and ambient temperature is essential to identify and predict the total energy available for use by a PV system 29 …”

Section: Introductionmentioning

confidence: 99%

Evaluation ofPVoutput in terms of environmental impact based on mathematical and artificial neural network models

Kazem

2020

Int J Energy Res

Self Cite

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This paper presents different prediction models for a grid-connected photovoltaic (GCPV) system based on long-term data sets. A 1.4 kW GCPV installed in Sohar, Oman, where measurements of the electrical and environmental parameters are taken every second for 5 years (from May 2014 to April 2019) to reduce uncertainty and improve the proposed model's accuracy. The highest power and energy measured from the GCPV are 1180 W and 245.8 kWh, respectively. Mathematical regression and cascade-feed forward propagation (CFNN) models for PV current, voltage, and power were proposed in terms of environmental impact. A NeuroSolutions package was used to propose GCPV models for electrical quantities. An evaluation criterion is defined, in this study, to assess the system's performance. The proposed models show excellent agreement with the measured experimental data. However, CFNN shows a superior accuracy compared with empirical and regression proposed models. The two models have proposed a dispute, but the model relating the solar irradiation and ambient temperature to GCPV current is more accurate. To evaluate and validate the proposed CFNN models, mean square error, MAE, root mean square error, and R 2 metrics have been used as a criterion and compared with different artificial neural networks models in the literature. The proposed CFNN is found to give the most accurate results in terms of MSE = 0.0007, MAE = 0.4310, and RMSE = 0.0290 and highest accuracy R 2 = 0.9999, which shows the effectiveness and feasibility of the proposed models.

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Analysis and forecasting of weather conditions in Oman for renewable energy applications

Cited by 41 publications

References 27 publications

A holistic review on energy forecasting using big data and deep learning models

A holistic review on energy forecasting using big data and deep learning models

Performance Analysis of Routing Protocols over WSN under the constraints of Natural Hazards using Data Logger

Evaluation ofPVoutput in terms of environmental impact based on mathematical and artificial neural network models

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