[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123877-en":3,"doc-seo-123877-105":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":4,"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},123877,137441390410,"Hazel","https://ap-avatar.wpscdn.com/avatar/2000252f4ab5702993?_k=1776741390130283984",8,"Research & Report","Machine learning autoencoder-based parameters prediction for solar power generation systems in smart grid - ORIGINAL RESEARCH","During the fourth energy revolution, artificial intelligence is required to meet rising energy demand while fossil fuel reserves diminish, driving the shift to smart grids. The study targets accurate prediction of solar power system parameters to reduce losses and increase generation capacity, since reliable parameter estimation is crucial for converting conventional grids into smart grids. An AI-based machine learning approach using LSTM predicts solar plant parameters, then is improved with CNN-LSTM and the proposed autoencoder-LSTM. Visual analysis and model comparison show autoencoder-LSTM delivers superior parameter prediction accuracy over CNN-LSTM and basic LSTM, enhancing resilience and resourcefulness of the smart power system.","Received: 8 July 2023 - Revised: 10 November 2023 - Accepted: 16 December 2023 - IET Smart Grid  \nDOI: 10.1049/stg2.12153  \nORIGINAL RESEARCH  \nMachine learning autoencoder‐based parameters prediction for solar power generation systems in smart grid  \nAhsan Zafar1  | Yanbo Che1 | Muhammad Faheem2  | Muhammad Abubakar1 | Shujaat Ali1 | Muhammad Shoaib Bhutta3   \n1Key Laboratory of Smart Grid of Ministry of Education, School of Electrical and Information Engineering, Tianjin University, Tianjin, China  \n2Department of Computing Sciences, School of Technology and Innovations, University of Vaasa, Vaasa, Finland  \n3School of Automobile Engineering, Guilin  \nUniversity of Aerospace Technology, Guilin, China  \nCorrespondence  \nMuhammad Faheem, Department of Computing Sciences, School of Technology and Innovations, University of Vaasa, Vaasa 65200, Finland. [Email: muhammad.faheem@uwasa.fi](Email: muhammad.faheem@uwasa.fi)  \nAbstract  \nDuring the fourth energy revolution, artificial intelligence implementation is necessary in all fields of technology to meet the increasing energy demands and address the diminishing fossil fuel reserves, necessitating the shift towards smart grids. The authors focus on predicting parameters accurately to minimise loss and improve power generation capacity in smart grids, given that accurate parameter prediction is essential for traditional power grid stations converting to smart grids. The authors employ an artificial intelligence‐based machine learning model, namely the long short‐term memory, to predict parameters of a solar power plant. After analysing the results obtained from the long short‐term memory model in graphical visualisation, the model is further improved using two different techniques namely, a convolutional neural network‐long short‐term memory and the authors proposed an autoencoder long short‐term memory. Comparing the results of these models, the study finds that autoencoder long short‐term memory outperforms the convolutional neural network‐long short‐term memory as well as simple long short‐term memory. Thus, the use of artificial intelligence in this study substantially enhances the precision of parameter prediction by augmenting the performance of rudimentary machine learning models, thereby facilitating the attainment of a resilient and resourceful power system that overcomes power losses and ameliorates production capacity in the context of Smart Grids.  \nKEYW ORDS  \npower grids, power system management, power system planning, solar power stations  \n1 | INTRODUCTION  \nRenewable energy is rapidly gaining importance as the fossil fuel resources are depleting. The inclusion of green energy sources into smart networks has become essential to ensuring a consistent and effective supply of electricity. However, to achieve the full potential of these resources, it is essential to control smart grids through advanced technologies like artificial intelligence (AI) [1] . Among different renewable energy sources, solar plants are significant suppliers to meet energy demands. Every day, these plants produce electricity, which is supplied to the nationwide grids [2] . However, it is difficult to anticipate the generation of energy accurately in solar facilities [3] . While  \nnumerous established techniques are used to forecast power generation and other factors, the need for improvement in accuracy persists [4] . Recent years have seen researchers utilised different machine learning (ML) obtained from various electricity or energy plants [5] . These models have been demonstrated to be quite successful at predicting and forecasting many factors [6, 7] . The LSTM model demonstrated the lowest error rate compared to various other time‐series forecasting models (Linear Regression, Autoregressive Integrated Moving Average [ARIMA], Seasonal Autoregressive Integrated Moving Average [SARIMA], Autoregressive Integrated Moving Average with Exogenous Variables [ARIMAX], Seasonal Autoregressive Integr","cbCaify2KyqIOelY","https://ap.wps.com/l/cbCaify2KyqIOelY","pdf",3937432,1,23,"English","en",105,"# Introduction\n## Renewable energy and smart grids\n## Solar power forecasting challenges\n## Prior machine learning approaches and motivation\n# Methodology and models","[{\"question\":\"Why is parameter prediction important for solar power systems in smart grids?\",\"answer\":\"Accurate parameter prediction minimizes power losses and improves generation capacity, which is essential when traditional power grid stations transition to smart grids.\"},{\"question\":\"What machine learning models are compared in the study?\",\"answer\":\"The study compares LSTM, CNN-LSTM, and an autoencoder-LSTM approach for predicting parameters of a solar power plant.\"},{\"question\":\"Which model performs best and what is the conclusion?\",\"answer\":\"Autoencoder-LSTM outperforms CNN-LSTM and basic LSTM, improving the precision of parameter prediction and supporting a more resilient power system.\"}]","Machine learning autoencoder-based parameters prediction for solar power generation systems in smart grid - 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