[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122774-en":3,"doc-seo-122774-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},122774,16904993612988,"Olivia Brown","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Evaluation of Machine Learning Models for Smart Grid Parameters - Performance Analysis of ARIMA and Bi-LSTM","Renewable energy integration into smart grids is critical for managing and forecasting energy production in the fourth energy revolution. This study applies machine learning, specifically ARIMA and bidirectional long short-term memory (Bi-LSTM), to predict next-year solar power output. Trained and tested on one year of real-time solar generation data, the models are evaluated using mean absolute error (MAE) and root mean squared error (RMSE). Bi-LSTM achieves higher accuracy than ARIMA and captures complex patterns and long-term dependencies in real-time series data, supporting more efficient, sustainable power systems.","sustainability   \nArticle  \nEvaluation of Machine Learning Models for Smart Grid Parameters: Performance Analysis of ARIMA and Bi-LSTM  \nYuanhua Chen 1,2, Muhammad Shoaib Bhutta 2, *, Muhammad Abubakar 3,*, Dingtian Xiao 2, Fahad M. Almasoudi 4, Hamad Naeem 5 and Muhammad Faheem 6  \nCitation: Chen, Y.; Bhutta, M.S.; Abubakar, M.; Xiao, D.; Almasoudi, F.M.; Naeem, H.; Faheem, M. Evaluation of Machine Learning Models for Smart Grid Parameters:  \nPerformance Analysis of ARIMA and Bi-LSTM. Sustainability 2023, 15, 8555 . [https://doi.org/10.3390/su15118555](https://doi.org/10.3390/su15118555)  \nAcademic Editor: Luis Hern¡ndez-Callejo  \nReceived: 8 April 2023  \nRevised: 18 May 2023  \nAccepted: 22 May 2023  \nPublished: 25 May 2023  \nCopyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \n1 College of Mechanical and Vehicle Engineering, Hunan University, Changsha 410082, China; [chenyuanhua202209@163.com](chenyuanhua202209@163.com)  \n2 School of Automobile Engineering, Guilin University of Aerospace Technology, Guilin 541004, China; [18487356130@163.com](18487356130@163.com)  \n3 Key Laboratory of Smart Grid of Ministry of Education, Tianjin University, Tianjin 300072, China  \n4 Department of Electrical Engineering, Faculty of Engineering, University of Tabuk, Tabuk 47913, Saudi Arabia; [falmasoudi@ut.edu.sa](falmasoudi@ut.edu.sa)  \n5 Department of Computer Science, King Faisal University, Hofuf 31982, Saudi Arabia; [hamadnaeemh@yahoo.com](hamadnaeemh@yahoo.com)  \n6 Department of Computing Technology and Innovations, University of Vaasa, 65200 Vaasa, Finland;  \nmuhammad.faheem@uwasa.fi  \n* Correspondence: [shoaib@guat.edu.cn](shoaib@guat.edu.cn) (M.S.B.); [mr_abubakar58@hotmail.com](mr_abubakar58@hotmail.com) (M.A.)  \nAbstract: The integration of renewable energy resources into smart grids has become increasingly important to address the challenges of managing and forecasting energy production in the fourth energy revolution. To this end, artificial intelligence (AI) has emerged as a powerful tool for improving energy production control and management. This study investigates the application of machine learning techniques, specifically ARIMA (auto-regressive integrated moving average) and Bi-LSTM (bidirectional long short-term memory) models, for predicting solar power production for the next year. Using one year of real-time solar power production data, this study trains and tests these modelson performance measures such as mean absolute error (MAE) and root mean squared error (RMSE) . The results demonstrate that the Bi-LSTM (bidirectional long short-term memory) model outperforms the ARIMA (auto-regressive integrated moving average) model in terms of accuracy and is able to successfully identify intricate patterns and long-term relationships in the real-time-series data. The findings suggest that machine learning techniques can optimize the integration of renewable energy resources into smart grids, leading to more efficient and sustainable power systems.  \nKeywords: renewable energy; smart grids; energy forecasting; ARIMA; Bi-LSTM model  \n1. Introduction  \nAs the world is moving towards sustainability, there is an increasing need for renewable and green energy resources. Non-renewable resources such as fossil fuels are rapidly disappearing, and their consumption is causing severe environmental hazards. Therefore, renewable energy resources such as solar energy, wind energy, and hydroelectric power are becoming more and more popular [1] . However, the integration of these resources into the smart grid is a challenge that needs to be addressed. Almost all researchers focus on enhancing stability and control in power grid systems through th","cbCaipZcMVVJUVGp","https://ap.wps.com/l/cbCaipZcMVVJUVGp","pdf",3153473,1,25,"English","en",105,"# Introduction\n## Renewable energy and smart grid forecasting\n## Machine learning approaches for prediction\n# Methodology\n## ARIMA model\n## Bi-LSTM model\n# Results and Performance Analysis\n## Error metrics (MAE, RMSE)\n## Model comparison\n# Conclusion","[{\"question\":\"What machine learning models are compared in the study?\",\"answer\":\"The study compares ARIMA with a bidirectional long short-term memory (Bi-LSTM) model for solar power prediction.\"},{\"question\":\"What data and evaluation metrics are used?\",\"answer\":\"Models are trained and tested using one year of real-time solar power production data and evaluated with MAE and RMSE.\"},{\"question\":\"Which model performs better and why?\",\"answer\":\"Bi-LSTM outperforms ARIMA in accuracy, because it can identify intricate patterns and long-term relationships in the real-time time series.\"}]","Evaluation of Machine Learning Models for Smart Grid Parameters - Performance Analysis of ARIMA and Bi-LSTM | PDF",1785812823,63,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"evaluation-of-machine-learning-models-for-smart-grid-parameters-performance-analysis-of-arima-and-bi-lstm","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/evaluation-of-machine-learning-models-for-smart-grid-parameters-performance-analysis-of-arima-and-bi-lstm/122774/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What machine learning models are compared in the study?","Question",{"text":75,"@type":76},"The study compares ARIMA with a bidirectional long short-term memory (Bi-LSTM) model for solar power prediction.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What data and evaluation metrics are used?",{"text":80,"@type":76},"Models are trained and tested using one year of real-time solar power production data and evaluated with MAE and RMSE.",{"name":82,"@type":73,"acceptedAnswer":83},"Which model performs better and why?",{"text":84,"@type":76},"Bi-LSTM outperforms ARIMA in accuracy, because it can identify intricate patterns and long-term relationships in the real-time time series.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]