Muhammad Aiqal Iskandar, Muhammad Azfar Shamil Abd Aziz, S. S. Sivaraju, Nurdiyana Borhan, Wan Abd Al-Qadr Imad Wan Mohtar, Nurfadzilah Ahmad
{"title":"使用人工神经网络(ANN)方法对马来西亚东部沿海地区的太阳能发电量进行长期预测","authors":"Muhammad Aiqal Iskandar, Muhammad Azfar Shamil Abd Aziz, S. S. Sivaraju, Nurdiyana Borhan, Wan Abd Al-Qadr Imad Wan Mohtar, Nurfadzilah Ahmad","doi":"10.37934/arfmts.117.2.6070","DOIUrl":null,"url":null,"abstract":"Accurate prediction of power demand and generation is crucial for modern energy systems to efficiently allocate resources and facilitate energy trading. The integration of artificial intelligence (AI) and machine learning techniques has significantly improved the precision of power forecasting. This study focuses on the application of Artificial Neural Networks (ANN) for forecasting power generation in the Eastern Coast region of Malaysia, with a specific emphasis on solar power. The research methodology involves collecting and analyzing historical power data, weather data, and relevant variables. ANN models are trained, validated, and tested on a selected power grid to assess their accuracy and predictive capabilities. The expected outcomes aim to include the development of a precise power generation forecasting model, providing valuable insights for decision-makers to optimize energy operations and seamlessly integrate renewable sources. Additionally, the study explores potential challenges, limitations, and best practices associated with ANN-based power forecasting. The dataset covers the period from 2020 to 2023, with variables such as average output power, ambient temperature, PV module temperature, global horizontal irradiance, and wind speed recorded at 30-minute intervals. The architecture of the ANN model, implemented using the Keras framework, is described as a Sequential model with layers utilizing the 'ReLU' activation function. Model evaluation employs metrics like root mean square error (RMSE), mean square error (MSE), and mean absolute error (MAE) on the test set, offering insights into the model's overall fit, average deviation, and sensitivity to outliers. Results reveal strong correlations between PV module temperature, irradiance, and AC power generated.","PeriodicalId":37460,"journal":{"name":"Journal of Advanced Research in Fluid Mechanics and Thermal Sciences","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Long-Term Solar Power Generation Forecasting in the Eastern Coast Region of Malaysia using Artificial Neural Network (ANN) Method\",\"authors\":\"Muhammad Aiqal Iskandar, Muhammad Azfar Shamil Abd Aziz, S. S. Sivaraju, Nurdiyana Borhan, Wan Abd Al-Qadr Imad Wan Mohtar, Nurfadzilah Ahmad\",\"doi\":\"10.37934/arfmts.117.2.6070\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Accurate prediction of power demand and generation is crucial for modern energy systems to efficiently allocate resources and facilitate energy trading. The integration of artificial intelligence (AI) and machine learning techniques has significantly improved the precision of power forecasting. This study focuses on the application of Artificial Neural Networks (ANN) for forecasting power generation in the Eastern Coast region of Malaysia, with a specific emphasis on solar power. The research methodology involves collecting and analyzing historical power data, weather data, and relevant variables. ANN models are trained, validated, and tested on a selected power grid to assess their accuracy and predictive capabilities. The expected outcomes aim to include the development of a precise power generation forecasting model, providing valuable insights for decision-makers to optimize energy operations and seamlessly integrate renewable sources. Additionally, the study explores potential challenges, limitations, and best practices associated with ANN-based power forecasting. The dataset covers the period from 2020 to 2023, with variables such as average output power, ambient temperature, PV module temperature, global horizontal irradiance, and wind speed recorded at 30-minute intervals. The architecture of the ANN model, implemented using the Keras framework, is described as a Sequential model with layers utilizing the 'ReLU' activation function. Model evaluation employs metrics like root mean square error (RMSE), mean square error (MSE), and mean absolute error (MAE) on the test set, offering insights into the model's overall fit, average deviation, and sensitivity to outliers. Results reveal strong correlations between PV module temperature, irradiance, and AC power generated.\",\"PeriodicalId\":37460,\"journal\":{\"name\":\"Journal of Advanced Research in Fluid Mechanics and Thermal Sciences\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Advanced Research in Fluid Mechanics and Thermal Sciences\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.37934/arfmts.117.2.6070\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"Chemical Engineering\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Advanced Research in Fluid Mechanics and Thermal Sciences","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.37934/arfmts.117.2.6070","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"Chemical Engineering","Score":null,"Total":0}
引用次数: 0
摘要
准确预测电力需求和发电量对于现代能源系统高效分配资源和促进能源交易至关重要。人工智能(AI)与机器学习技术的结合大大提高了电力预测的精确度。本研究的重点是应用人工神经网络(ANN)预测马来西亚东部沿海地区的发电量,尤其侧重于太阳能发电。研究方法包括收集和分析历史电力数据、天气数据和相关变量。在选定的电网上对 ANN 模型进行训练、验证和测试,以评估其准确性和预测能力。预期成果旨在开发一个精确的发电预测模型,为决策者优化能源运营和无缝集成可再生能源提供有价值的见解。此外,本研究还探讨了与基于 ANN 的电力预测相关的潜在挑战、局限性和最佳实践。数据集涵盖 2020 年至 2023 年,每 30 分钟记录一次平均输出功率、环境温度、光伏组件温度、全球水平辐照度和风速等变量。使用 Keras 框架实现的 ANN 模型的架构被描述为一个序列模型,各层使用 "ReLU "激活函数。模型评估采用了测试集上的均方根误差 (RMSE)、均方误差 (MSE) 和平均绝对误差 (MAE) 等指标,以深入了解模型的整体拟合度、平均偏差和对异常值的敏感性。结果显示,光伏模块温度、辐照度和交流发电量之间存在很强的相关性。
Long-Term Solar Power Generation Forecasting in the Eastern Coast Region of Malaysia using Artificial Neural Network (ANN) Method
Accurate prediction of power demand and generation is crucial for modern energy systems to efficiently allocate resources and facilitate energy trading. The integration of artificial intelligence (AI) and machine learning techniques has significantly improved the precision of power forecasting. This study focuses on the application of Artificial Neural Networks (ANN) for forecasting power generation in the Eastern Coast region of Malaysia, with a specific emphasis on solar power. The research methodology involves collecting and analyzing historical power data, weather data, and relevant variables. ANN models are trained, validated, and tested on a selected power grid to assess their accuracy and predictive capabilities. The expected outcomes aim to include the development of a precise power generation forecasting model, providing valuable insights for decision-makers to optimize energy operations and seamlessly integrate renewable sources. Additionally, the study explores potential challenges, limitations, and best practices associated with ANN-based power forecasting. The dataset covers the period from 2020 to 2023, with variables such as average output power, ambient temperature, PV module temperature, global horizontal irradiance, and wind speed recorded at 30-minute intervals. The architecture of the ANN model, implemented using the Keras framework, is described as a Sequential model with layers utilizing the 'ReLU' activation function. Model evaluation employs metrics like root mean square error (RMSE), mean square error (MSE), and mean absolute error (MAE) on the test set, offering insights into the model's overall fit, average deviation, and sensitivity to outliers. Results reveal strong correlations between PV module temperature, irradiance, and AC power generated.
期刊介绍:
This journal welcomes high-quality original contributions on experimental, computational, and physical aspects of fluid mechanics and thermal sciences relevant to engineering or the environment, multiphase and microscale flows, microscale electronic and mechanical systems; medical and biological systems; and thermal and flow control in both the internal and external environment.