Estimating Solar Irradiance on Tilted Surface with Arbitrary Orientations and Tilt Angles

Cheng, Hsu-Yung; Yu, Chih Chang; Hsu, Kuo Chang; Chan, Chun-Kit; Tseng, Mei‐Hui; Lin, Chih Lung

doi:10.3390/en12081427

Cited by 13 publications

(6 citation statements)

References 27 publications

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“…As mentioned, the other factors which could be damaging the Pearson correlation between horizontal irradiation and energy production (the different orientation and tilt of the panels and the more blocked horizon) could have been corrected with the implementation in the model of the solar angles (zenith and azimuth), the meteorological variables (temperature and humidity) and the calendar data. The solar angles help to predict the relationship between the horizontal irradiation and the irradiation in the plane of the panels [68], which is reinforced with the meteorological variables [63] that also influence other factors, such as the efficiency of the panels [69].…”

Section: Forecasting Resultsmentioning

confidence: 93%

“…The differences in the correlations obtained may be due to several factors. First, the sky conditions in Konstanz are much more diverse than the sky conditions in Alice Springs, which has an impact in the relation between horizontal irradiation and plane-of-array (POA) irradiation [63], and therefore, in energy production. Secondly, the GE_1 and GE_3 installations are not oriented towards the south, but slightly towards the southwest, which produces a certain gap between the maximum generation and the maximum irradiation points, as shown in Figure 7.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Application of Temporal Fusion Transformer for Day-Ahead PV Power Forecasting

Santos¹,

García-Santiago²,

Camarero³

et al. 2022

Energies

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The energy generated by a solar photovoltaic (PV) system depends on uncontrollable factors, including weather conditions and solar irradiation, which leads to uncertainty in the power output. Forecast PV power generation is vital to improve grid stability and balance the energy supply and demand. This study aims to predict hourly day-ahead PV power generation by applying Temporal Fusion Transformer (TFT), a new attention-based architecture that incorporates an interpretable explanation of temporal dynamics and high-performance forecasting over multiple horizons. The proposed forecasting model has been trained and tested using data from six different facilities located in Germany and Australia. The results have been compared with other algorithms like Auto Regressive Integrated Moving Average (ARIMA), Long Short-Term Memory (LSTM), Multi-Layer Perceptron (MLP), and Extreme Gradient Boosting (XGBoost), using statistical error indicators. The use of TFT has been shown to be more accurate than the rest of the algorithms to forecast PV generation in the aforementioned facilities.

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Section: Forecasting Resultsmentioning

confidence: 93%

Section: Discussionmentioning

confidence: 99%

Application of Temporal Fusion Transformer for Day-Ahead PV Power Forecasting

Santos¹,

García-Santiago²,

Camarero³

et al. 2022

Energies

View full text Add to dashboard Cite

show abstract

“…The model consisted of three hidden layers, each one containing six neurons, total mean nRMSE of 8.02 %. 33 It used meteorological data gathered over a 10-year period from five distinct places throughout India to train the models based on different methods for forecasting monthly average global solar radiation. 34 A backpropagation neural network (BPNN) was used to predict solar irradiance data in different spectral bands from daily time series.…”

Section: Related Studiesmentioning

confidence: 99%

Prediction of Hourly Global Solar Radiation: Comparison of Neural Networks / Bootstrap Aggregating

Dahmani

Ammi

Hanini

et al. 2023

Kemija U Industriji

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This research work explores the use of single neural networks and bootstrap aggregated neural networks for predicting hourly global solar radiation. A database of 3606 data points were from the Renewable Energies Development Center, radiometric station 'Shems' of Bouzareah. The single neural networks and bootstrap aggregated neural networks were built together. The precision and durability of neural network models generated with an incomplete quantity of training datasets were improved using bootstrap aggregated neural networks. To produce numerous sets of training data points, the training data was re-sampled utilising bootstrap resampling by replacement. A neural network model was built for each of the data points. The individual neural network models were then combined to produce the bootstrap aggregated neural networks. The experimental and predicted values of global solar radiation were compared, and lower root mean squared errors (68.3968 and 62.4856 Wh m −2 ) were discovered during the testing phases for single neural networks and bootstrap aggregated neural networks, respectively. The results of these models show that the bootstrap aggregated neural networks model is more accurate and robust than single neural networks.

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“…Solar radiation is the key factor determining the electricity production of BIPV systems. To make better use of BIPV systems, the variation and maximum utilization of solar radiation incident on solar photovoltaic (PV) panels are valuable in the design stage [11]. Solar radiation data are necessary for evaluating the profitability in installing a BIPV system [12].…”

Section: Introductionmentioning

confidence: 99%

An improved method for direct incident solar radiation calculation from hourly solar insolation data in building energy simulation

Yan

Guo

et al. 2020

Energy and Buildings

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Solar radiation considerably influences the energy consumption of buildings and the power production of building integrated photovoltaic (BIPV) systems. Hourly solar insolation (Wh/m 2), represented as the amount of solar irradiance collected on the ground during a 1-h period, is the most common solar radiation data available and widely used in weather files applied in building energy modeling programs (BEMPs). Because the solar beam and position vary over time, the use of hourly insolation data as the input might result in errors in the estimation of the direct incident solar radiation on a particular surface. In this study, methods used in BEMPs for direct incident solar radiation calculations are first analyzed, and an improved method adopting a new algorithm for estimating the solar irradiance is proposed. The algorithm assumes that the solar irradiance changes linearly within a 1-h period and can be estimated based on the solar irradiance at the half clock and slope. The collected direct normal solar irradiance data of 2016 from eight solar radiation stations in China were used to demonstrate the proposed method and evaluate its performance by comparing the results with those from three conventional methods used in BEMPs along with the ground truth measurements. In addition, in this study, factors affecting the accuracy of the calculation results are explored. The results of the estimated direct incident solar radiation show that the proposed method achieves the best accuracy, followed by the methods used in DOE-2, EnergyPlus, and DeST. The proposed method guarantees that the hourly direct solar insolation will remain the same and reflects the variation in the direct solar irradiance across a 1-h time frame. The proposed method can be adopted in BEMPs to improve the accuracy of the solar radiation calculation, thereby improving the accuracy of the simulated building performance and the BIPV production.

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Estimating Solar Irradiance on Tilted Surface with Arbitrary Orientations and Tilt Angles

Cited by 13 publications

References 27 publications

Application of Temporal Fusion Transformer for Day-Ahead PV Power Forecasting

Application of Temporal Fusion Transformer for Day-Ahead PV Power Forecasting

Prediction of Hourly Global Solar Radiation: Comparison of Neural Networks / Bootstrap Aggregating

An improved method for direct incident solar radiation calculation from hourly solar insolation data in building energy simulation

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