40 Table 4.2: The final parameters of ANN model selected as optimized model.47 Table 4.3: The final losses and errors of the ANN model developed when the epoch is 1300. 43 Figure 4.9: TelePower monthly optimal ISR calculated using ground-based measurement and satellite-derived irradiance datasets.
General Introduction
Coverage area from different online. the source of the satellite-derived solar radiation datasets is shown in Figure 1.2. However, the accuracy of the optimal ISR value simulated from satellite-derived datasets may not be as high compared to the optimal ISR value calculated from ground-based solar radiation datasets.
Problem Statement
For example, solar irradiance datasets acquired in a 1-hour interval may miss the peak irradiance value in that time interval and affect the optimal ISR result. Therefore, this project is undertaken to overcome this shortcoming of satellite solar irradiance datasets and to make the optimal ISR simulated by satellite datasets closer to the optimal ISR obtained from ground-based datasets.
Importance and Contribution of the Study
This is because the relationship between the optimal ISR simulated by ground-based and satellite datasets is complex and the effect of the parameters used in the calculation of the optimal ISR is still unpredictable. Thus, in this project, an ANN model will be developed to infer optimal ISR using satellite-derived data sets.
Aims and Objectives
More investors are therefore willing to participate in large-scale solar farms and this can help the government to promote renewable energy indirectly. This study therefore helps to promote the adoption of solar energy which can slow down this burning rate.
Scope and Limitation of the Study
Choosing the right inverter size is important to achieve a cost-efficient PV system design, giving the investors a shorter payback period.
Outline of the report
Solar Irradiance in Tropical Area
The solar irradiance distribution pattern in Malaysia can be observed with this file, which will be helpful in inferring the optimal ISR in other locations. However, this small change can affect the value of the optimal ISR of the PV system.
Inverter Sizing Ratio (ISR)
A research paper by Väisänen et al (2019) investigated the effect of time resolution of solar irradiance on the optimal aspect ratio. Thus, the research paper states that it is beneficial to use solar irradiance with less time resolution to find the optimal ISR.
Levelized Cost of Electricity (LCOE)
Optimal Inverter Sizing Ratio
Artificial Neural Network (ANN)
In the equation (3.5), the 𝜂𝑖𝑛𝑣(𝑙) is called a series of numbers selected from the efficiency curve of the inverter in the PVsyst software. First, two types of the data will be the input data of the ANN model, which are the monthly solar radiation and the optimal ISR calculated from satellite-derived datasets. The Figure 3.6 below showed an example of error graph after the training of the ANN model was done.
After training and optimization of the ANN is complete, the best performing ANN model will be used to derive the monthly optimal ISR from other sites. The input of the ANN model is the monthly irradiance of the specific location and their respective monthly optimal ISR value, simulated by satellite-derived datasets. Table 4.3 shows the final losses and errors of the ANN model when the epoch is 1300.
Thus, it can be concluded that the developed ANN model is capable of using satellite ISR with monthly irradiance of locations to infer ground-based ISR where sites do not have ground-based solar irradiance datasets. The relationship between the optimal ISR calculated from ground-based measurements and the satellite-derived irradiance data sets was investigated with the type of irradiance sensor used and the irradiance distribution profile of 10 sites.
Inference
Introduction
The relationship between the optimal ISR simulated by ground-based and satellite data sets is complex. The first phase mainly deals with solar irradiance data from ground-based and satellite datasets. The second stage is the calculation of the monthly optimal value of ISR and monthly irradiance, which will be used in the training and testing of the ANN model.
Finally, the fifth stage is to derive the monthly optimal ISR from other sites that do not have ground-based measurement of solar radiation.
Retrieving solar irradiance datasets
Software and programming language used
Interpolation on missing data
At the end of testing the interpolation function, the linear interpolation function has the smallest error among the tested functions. A comparison of the sum of errors between the tested interpolation functions is shown in Figure 3.2. Thus, the linear interpolation function from the Pandas library is used to interpolate the missing data in the raw solar irradiance data.
However, there are some when the missing data are too many (more than 10 consecutive time interval), or the missing data are located at the beginning or end of the day (where the start and end point cannot be estimated), that day with missing data will not be interpolated.
Calculate LCOE to determine optimal ISR
First, the AC electricity yield per day of a PV system is calculated based on the formula below: (Lai and Lim, 2019). In addition, the monthly LCOE of the system is derived in the following equations: (Lai and Lim, 2019). 𝑃𝑟𝑖𝑐𝑒𝑃𝑉 𝑠𝑦𝑠𝑡𝑒𝑚 = market price of PV system, RM/W 𝑃𝑟𝑖𝑐𝑒𝑃𝑉 𝑚𝑜𝑑𝑢𝑙𝑒 = market price of PV module, RM/W.
The plant's capital costs are the costs after achieving the saved costs of reduced inverter.
Separate available datasets into training and testing data group
Repeat the calculation of the LCOE and the monthly optimal ISR with the satellite-derived irradiance datasets co-located with the ground-based measurements of the solar irradiance datasets. 80% of the available datasets (approximately 80 datasets) will be chosen as the first group, helping to train the ANN and exploring the relationship between optimal ISR calculations based on ground-based datasets and satellite-derived data sets. The remaining 20% of the datasets (approximately 19 datasets) will be chosen as the second group that will test and evaluate the accuracy of the ANN by providing only the optimal ISR calculated from satellite-derived datasets.
The evaluation of the ANN model will be done by comparing the ANN output with the original optimal ISR value calculated from ground-based datasets.
Construction of Artificial Neural Network (ANN)
In a neural network, the activation function is responsible for transforming the summed weighted input from the node into the activation of the node. It uses squared gradients to scale the learning rate like RMSprop and takes advantage of momentum by using the moving average of the gradient instead of the gradient itself like SGD with momentum, which makes the modeling process less memory intensive and converges more quickly quickly to the minimum. (which give the lowest error between the predicted and the target value). Furthermore, it should be noted that during the training phase of the ANN model, 20% of the training data sets are extracted from the training data sets for validation purposes at the end of each epoch.
Then, the history data of the ANN model is recorded and the graph of validation loss and error versus epoch is plotted.
Optimization of ANN model
In addition, a graph showed the comparison between the predicted value and the target value of the test data sets and the training data sets is also recorded and plotted using the Python Matplot library as shown in Figure 3.7 below. In addition, the percent difference between the predicted and the actual value is calculated for the training data sets and the test data sets.
Infer the optimal ISR to other sites
Then, the percentage difference between the predicted monthly optimum ISR value and the actual monthly optimum ISR simulated with the co-located ground-based datasets is calculated.
Evaluation of ANN model performance
Milestone
Gantt Chart
Introduction
Summary of irradiance datasets from available sites
Comparison between optimal ISR computed from ground-based
Referring to the plot of optimal monthly ISR calculated using ground-based measurements and satellite-derived radiation datasets, 7 out of 10 sites disagree with the statement. Taking the UTAR and BKH locations as the reference of the common case where the satellite ISR is higher than the terrestrial ISR, it can be observed that the irradiance in the high irradiance range of the ground-based measurement datasets is smaller than the of satellite derived data on solar radiation distribution of AxisBC and Balakong countries. Referring to the solar irradiance distribution plot of AxisBC and Balakong, it can be observed that the high solar irradiance in the ground-based measurement datasets has a value close to zero.
At the same time, the solar irradiance sensors used at AxisBC and Balakong sites are both photodiode sensors, which are widely used in agriculture.
Final ANN model after the optimization is done
Step loss and validation loss converge to a small constant value after epoch 200 and continue to fluctuate until the default epoch 1300.
Comparison between inferred optimal ISR and the actual optimal
From the table in Appendix C, the average percent difference between the prediction and the actual value of the ground ISR is 5.6%, which is less than 6%. In other words, the difference between satellite ISR and its corresponding ground-based ISR is reduced. From Table 4.4, the average percent difference between the prediction and the actual value of ground ISR using the testing data sets is 5.8%, which is less than 6%.
The range of percentage differences is reduced by using an ANN model developed to predict the ground ISR of a particular site.
Suggestions to improve the performance of ANN model based on
To increase the reliability of the ANN model, the geographic location of locations for measuring and collecting solar irradiance datasets should be disseminated in Malaysia at a distance. Thus, the built ANN model can be trained with more patterns of solar irradiance distribution and improve its performance when used to derive optimal ISR from other locations. A total number of 99 data sets is insufficient for the ANN model to draw a conclusion about the relationship between the ground ISR and the satellite ISR value.
Referring to the working principle of the ANN model and the explanation of the inference in Section 2.5 and 2.6, a larger number of data sets can increase the accuracy to derive the optimal ISR value.
Conclusion
Recommendation
Python AI: How to build a neural network and make predictions, [online] Available at: < https://realpython.com/python-ai-neural- network/#computing-the-prediction-error> [Accessed 29 August 2021 ]. Available at: UNECE Wiki <. https://wiki.unece.org/download/attachments/25267247/SOLAR%2BGUIDE. 2022) 'A tropical region case study for evaluating univariate imputation methods for solar irradiance data with different weather types', Sustainable Energy Technologies and Assessments, 50 (October 2021), pp. Neural Network Models (Supervised) [online] Available at: < https:/ /scikit-learn.org/stable/modules/neural_networks_supervised.html >.
Levelized costs of new generation sources in the 2021 Annual Energy Outlook, [online] Available at: < https://www.eia.gov/outlooks/aeo/pdf/electricity_generation.pdf>. Theor_Power_af_Inv = result_df ['Theor_Power_bf_Inv'] * result_df ['Inv_eff_from_loading']. Overpwr_inv_cap, 'Theor_Power_af_Clipping'] = Overpwr_inv_cap result_df['Theor_Power_af_Clipping'] = result_df['Theor_Power_af_Clipping'].fillna(result_df['Theor_Power_af_Inv']).
