Solar radiation forecasting with deep learning techniques integrating geostationary satellite images

Gallo, Raimondo; Castangia, Marco; Macii, Alberto; Macii, Enrico; Patti, Edoardo; Aliberti, Alessandro

doi:10.1016/j.engappai.2022.105493

Cited by 22 publications

(8 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Figure 5 shows the modelling of TC, MRC and RC, where the values of the following were needed, (1) coil inductance, Lc, (2) coil resistance, Rc, and (3) mutual inductance, M. In this process, the solenoid coil was used, with its diameter (dc), number of turns (Nc), and length (lc) obtained through Equations ( 2) and (3). In addition, Rc was obtained using Equation ( 5) [2], [3]. The TC and RC had a distance of dtr, with the MRC positioned in the center of both coils (Figure 1).…”

Section: Modelling Of Transmitter Magnetic-relay and Receiver Coilmentioning

confidence: 99%

“…Solar radiation is a sun fusion process that produces and emits energy to the surface of the earth. This radiation is divided into two parts, namely (1) solar irradiance, the sun power density with a unit of W/m 2 , and (2) solar irradiation, the sun energy density with a unit of J/m 2 or Wh/m 2 [1][2][3]. The component of solar irradiance is very important in the conversion of sunlight to electrical energy, using a PV module [4][5][6], which has a wide global application.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Effect of Temperature and Solar Irradiance on the Performance of 50 Hz Photovoltaic Wireless Power Transfer System

et al. 2023

View full text Add to dashboard Cite

A wireless power transfer (WPT) system transfers an alternating current (AC) power from a TC (transmitter coil) to a RC (receiver coil), using an electromagnetic field concept. Several previous reports on WPT systems were able to transmit this power, although their AC frequencies were high in some kilohertz or megahertz (kHz or MHz). These frequencies were unable to be applied directly on the AC loads or only administered to the direct current (DC) energy after rectification through a rectifier circuit. In the receiver phase, the AC power was also very low, due to the minimum voltage and current on the RC. Therefore, this study aims to determine the effects of temperature and solar irradiance on the performance of the 50 Hz photovoltaic wireless power transfer (PVWPT) system. This system is constructed by some PV modules in series connection, to achieve a required DC voltage level on the DC-DC converter and receiver phase. It also converts DC to AC voltages on the TC (transmitter coil) of an H-bridge inverter, with a receiver circuit and a magnetic relay coil positioned between the TC and RC (transmitter and receiver coil). The results showed that solar irradiance and temperature affected the performances of the PV module and PVWPT system. This indicated that higher solar irradiance increased the performance of the PVWPT system, whose maximum efficiency was achieved by positioning the magnetic relay coil between the TC and RC.

show abstract

Section: Modelling Of Transmitter Magnetic-relay and Receiver Coilmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Effect of Temperature and Solar Irradiance on the Performance of 50 Hz Photovoltaic Wireless Power Transfer System

et al. 2023

View full text Add to dashboard Cite

show abstract

“…Although these three locations are geographically close, there is a significant difference in their solar energy generation capacity. In recent years, due to the flourishing development of machine learning, the accuracy of climate prediction has significantly improved [3][4][5][6][7][8][9]. Lauret et al [10] used a model to predict solar radiation in island environments and proposed the use of machine learning models to enhance the performance of linear regression models.…”

Section: Introductionmentioning

confidence: 99%

“…This paper reports on a new hybrid deep residual learning and gated longshort-term memory recurrent network boosted via a differential covariance matrix adaptation evolution strategy (ADCMA) to forecast solar radiation one hour ahead. Neshat et al [15] reported on a hybrid deep residual learning and gated long-short-term memory In recent years, due to the flourishing development of machine learning, the accuracy of climate prediction has significantly improved [3][4][5][6][7][8][9]. Lauret et al [10] used a model to predict solar radiation in island environments and proposed the use of machine learning models to enhance the performance of linear regression models.…”

Section: Introductionmentioning

confidence: 99%

A Global Solar Radiation Forecasting System Using Combined Supervised and Unsupervised Learning Models

Wei,

Yang

2023

Energies

View full text Add to dashboard Cite

One of the most important sources of energy is the sun. Taiwan is located at a 22–25° north latitude. Due to its proximity to the equator, it experiences only a small angle of sunlight incidence. Its unique geographical location can obtain sustainable and stable solar resources. This study uses research on solar radiation forecasts to maximize the benefits of solar power generation, and it develops methods that can predict future solar radiation patterns to help reduce the costs of solar power generation. This study built supervised machine learning models, known as a deep neural network (DNN) and a long–short-term memory neural network (LSTM). A hybrid supervised and unsupervised model, namely a cluster-based artificial neural network (k-means clustering- and fuzzy C-means clustering-based models) was developed. After establishing these models, the study evaluated their prediction results. For different prediction periods, the study selected the best-performing model based on the results and proposed combining them to establish a real-time-updated solar radiation forecast system capable of predicting the next 12 h. The study area covered Kaohsiung, Hualien, and Penghu in Taiwan. Data from ground stations of the Central Weather Administration, collected between 1993 and 2021, as well as the solar angle parameters of each station, were used as input data for the model. The results of this study show that different models offer advantages and disadvantages in predicting different future times. The hybrid prediction system can predict future solar radiation more accurately than a single model.

show abstract

“…In terms of modeling solar radiation intensity, many studies have investigated the scatter scheme of the intensity of solar radiation in different locations using various methods and approaches. The other modeling techniques have been implemented by a combination method of empirical techniques and machine learning [2], deep learning model [3], six machine learning algorithms for daily global solar radiation and air temperature [4], multivariate time series based on NN method [5], ensemble artificial intelligence [6], and deep learning approach [7]. Meanwhile, those based on (static) neural network models include the ANN model with different learning algorithms [8], the modeling of solar radiation intensity in Mataram City, the hybrid wavelet-based model in Lombok Island, and SNN [9].…”

Section: Introductionmentioning

confidence: 99%

Enhanced Performance of Dynamic Neural Network Model using Wavelet Activation Functions

Bahri,

Awalushaumi,

Fitriyani

2023

LKJITI

View full text Add to dashboard Cite

Both static and dynamic adaptive neural networks have been broadly utilized in mathematical modeling and numerical analysis. This study aimed to enhance the accomplishment of Dynamic Neural Networks (DNN) models by applying wavelet functions as activation functions. Research that models and forecasts the intensity of solar radiation in Mataram City shows that combining B-Spline and Morlet wavelet activation functions can significantly increase the DNN model performance. Wavelet-DNN (W-DNN) was modeled with an identical architecture; the best showed the increase in the model achievement (0.7596 points for in-sample and 0.8502 points for out-sample data). Mainly for out-sample data, the model's performance using the W-DNN+ intervention model increased by 4.0492 points.

show abstract

Solar radiation forecasting with deep learning techniques integrating geostationary satellite images

Cited by 22 publications

References 32 publications

Effect of Temperature and Solar Irradiance on the Performance of 50 Hz Photovoltaic Wireless Power Transfer System

Effect of Temperature and Solar Irradiance on the Performance of 50 Hz Photovoltaic Wireless Power Transfer System

A Global Solar Radiation Forecasting System Using Combined Supervised and Unsupervised Learning Models

Enhanced Performance of Dynamic Neural Network Model using Wavelet Activation Functions

Contact Info

Product

Resources

About