The Importance of Model Evaluation

Oct 13, 2025 by sorusgentrification.com 33 views

Comparison of Sun Radiation Predictions Using Gated Recurrent Unit (GRU), Long Short-Term Memory (LSTM), and Deep Q-Network (DQN) Algorithm

Introduction

Sun radiation predictions play a crucial role in providing detailed information about diesel resources in a region, which is beneficial for entrepreneurs, investors, and the government in developing large-scale diesel energy. With accurate information, they can make better decisions related to investment and diesel energy policy in an area. Various methods have been used to predict sunlight radiation, ranging from physical models such as Numerical Weather Prediction (NWP), statistical models, to artificial intelligence models. This study aims to compare three algorithms, namely the Gated Recurrent Unit (GRU), Long Short-Term Memory (LSTM), and Deep Q-Network (DQN), to determine the best algorithm in predicting sunlight radiation.

Background

Sun radiation predictions are essential for the development of renewable energy, particularly in Indonesia. The data used in this study came from the results of the study of software development to estimate solar radiation in the bright sky in Indonesia by Himsar Ambarita (2017), with data coverage for seven years. The main purpose of this study is to compare regression algorithm accuracy such as GRU and LSTM with DQN algorithms that have calculations based on reward values in predicting sunlight radiation.

Methodology Analysis

The methodology used in this study involves three algorithms, each of which has advantages and disadvantages. GRU and LSTM are artificial neural networks that are designed to process sequential data, very suitable for time data analysis, such as sun radiation predictions. Both are able to remember important information for a long time, despite having a different approach to handling this information. GRU, for example, is simpler than LSTM, so it is faster in terms of training and has fewer parameter needs.

On the other hand, DQN implements reinforcement learning based on reward values. This method focuses on making decisions in certain situations and can be adaptive to changes in data over time. DQN utilizes previous experiences to increase the accuracy of its prediction, but at the same time requires more data and time for training compared to regression-based methods.

The Importance of Model Evaluation

In this comparison, it is essential to use several evaluation metrics to get a comprehensive picture of the performance of each algorithm. Mean Squared Error (MSE) and Root Mean Squared Error (RMSE) provide information about how much prediction error, while Mean Absolute Error (MAE) shows an average error without disturbing extreme values. R² gives a picture of how well the model explains the variation in the data.

The use of some of these metrics is important so that not only one aspect is the focus in the model assessment. Thus, if one algorithm shows good results in one metric but bad in another metric, researchers can reconsider their decisions based on more holistic evaluations.

Comparison of Algorithms

The comparison of GRU, LSTM, and DQN algorithms in predicting sunlight radiation is crucial in determining the best algorithm. The results of this study will provide useful insights for decision makers in investment and solar energy policy. The accuracy of predictions is not the only focus, but also the effectiveness and efficiency of the methods used in a broader context.

Advantages and Disadvantages of Each Algorithm

GRU has several advantages, including simplicity, faster training, and fewer parameter needs. However, it may not be able to handle complex data as well as LSTM. LSTM, on the other hand, has several advantages, including the ability to handle complex data and remember important information for a long time. However, it may be slower in terms of training and require more parameters.

DQN has several advantages, including the ability to adapt to changes in data over time and utilize previous experiences to increase the accuracy of its prediction. However, it may require more data and time for training compared to regression-based methods.

Conclusion

The ratio between the GRU, LSTM, and DQN algorithms in predicting sunlight radiation has a high relevance, especially in the context of the development of renewable energy in Indonesia. By understanding the strengths and weaknesses of each algorithm and evaluating the model as a whole, it is hoped that this research can provide useful insights for decision makers in investment and solar energy policy.

Future Research Directions

This study provides a comprehensive comparison of GRU, LSTM, and DQN algorithms in predicting sunlight radiation. However, there are several future research directions that can be explored, including:

Comparing the performance of GRU, LSTM, and DQN algorithms in predicting sunlight radiation in different regions.
Investigating the effect of different evaluation metrics on the performance of GRU, LSTM, and DQN algorithms.
Developing new algorithms that can handle complex data and provide more accurate predictions.

Limitations of the Study

This study has several limitations, including:

The use of a limited dataset, which may not be representative of the entire region.
The comparison of only three algorithms, which may not be comprehensive.
The use of a single evaluation metric, which may not provide a complete picture of the performance of each algorithm.

Recommendations for Future Research

Based on the findings of this study, several recommendations can be made for future research, including:

Using a larger and more representative dataset to evaluate the performance of GRU, LSTM, and DQN algorithms.
Comparing the performance of GRU, LSTM, and DQN algorithms in predicting sunlight radiation in different regions.
Developing new algorithms that can handle complex data and provide more accurate predictions.

Conclusion

In conclusion, this study provides a comprehensive comparison of GRU, LSTM, and DQN algorithms in predicting sunlight radiation. The results of this study will provide useful insights for decision makers in investment and solar energy policy. The accuracy of predictions is not the only focus, but also the effectiveness and efficiency of the methods used in a broader context.
Q&A: Comparison of Sun Radiation Predictions Using Gated Recurrent Unit (GRU), Long Short-Term Memory (LSTM), and Deep Q-Network (DQN) Algorithm

Q: What is the main purpose of this study?

A: The main purpose of this study is to compare the accuracy of three algorithms, namely the Gated Recurrent Unit (GRU), Long Short-Term Memory (LSTM), and Deep Q-Network (DQN), in predicting sunlight radiation.

Q: What are the advantages and disadvantages of each algorithm?

A: GRU has several advantages, including simplicity, faster training, and fewer parameter needs. However, it may not be able to handle complex data as well as LSTM. LSTM, on the other hand, has several advantages, including the ability to handle complex data and remember important information for a long time. However, it may be slower in terms of training and require more parameters. DQN has several advantages, including the ability to adapt to changes in data over time and utilize previous experiences to increase the accuracy of its prediction. However, it may require more data and time for training compared to regression-based methods.

Q: What are the evaluation metrics used in this study?

A: The evaluation metrics used in this study include Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and R² (the coefficient of determination).

Q: Why is it essential to use multiple evaluation metrics?

A: It is essential to use multiple evaluation metrics to get a comprehensive picture of the performance of each algorithm. Using only one metric may not provide a complete picture of the performance of each algorithm.

Q: What are the limitations of this study?

A: The limitations of this study include the use of a limited dataset, which may not be representative of the entire region, and the comparison of only three algorithms, which may not be comprehensive.

Q: What are the recommendations for future research?

A: Based on the findings of this study, several recommendations can be made for future research, including using a larger and more representative dataset to evaluate the performance of GRU, LSTM, and DQN algorithms, comparing the performance of GRU, LSTM, and DQN algorithms in predicting sunlight radiation in different regions, and developing new algorithms that can handle complex data and provide more accurate predictions.

Q: What are the implications of this study for decision makers in investment and solar energy policy?

A: The results of this study will provide useful insights for decision makers in investment and solar energy policy. The accuracy of predictions is not the only focus, but also the effectiveness and efficiency of the methods used in a broader context.

Q: What are the potential applications of this study?

A: The potential applications of this study include predicting sunlight radiation for solar energy generation, optimizing solar panel placement, and improving the accuracy of solar radiation forecasts.

Q: What are the future research directions?

A: Several future research directions can be explored, including comparing the performance of GRU, LSTM, and DQN algorithms in predicting sunlight radiation in different regions, investigating the effect of different evaluation metrics on the performance of GRU, LSTM, and DQN algorithms, and developing new algorithms that can handle complex data and provide more accurate predictions.

Q: What are the potential challenges and limitations of this study?

A: The potential challenges and limitations of this study include the use of a limited dataset, the comparison of only three algorithms, and the use of a single evaluation metric. Additionally, the study may be affected by the quality of the data used, the complexity of the algorithms, and the computational resources available.

Q: What are the potential benefits of this study?

A: The potential benefits of this study include providing a comprehensive comparison of GRU, LSTM, and DQN algorithms in predicting sunlight radiation, improving the accuracy of solar radiation forecasts, and providing useful insights for decision makers in investment and solar energy policy.