[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120181-en":3,"doc-seo-120181-105":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},120181,1099514068035,"Ezra","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Optimizing Energy Storage System (ESS) for Charging and Discharging Operations via Machine Learning - Master’s Thesis","Powerpal faces inefficient energy distribution: its energy storage system (ESS) is charged to full capacity at midnight, which may reduce efficiency and profit. This thesis investigates whether machine learning improves charging and discharging efficiency and cost effectiveness by forecasting energy demand from electric vehicles and generation from solar and the grid. Two LSTM variants (univariate and multivariate) are trained on Powerpal time-series data, with constant hyperparameters for fair comparison. Univariate LSTM yields more accurate predictions and supports cost analysis, showing greater savings for ESS operations in April 2024.","MUHAMMAD IMRAN HAIDER ABID  \nDEPARTMENT OF ENERGY RESOURCES  \nOptimizing Energy Storage System (ESS) for Charging and Discharging Operations via Machine Learning  \nMaster's Thesis-Computational Engineering-June 2024  \nI, Muhammad Imran Haider Abid, declare that this thesis titled,“Optimizing Energy Storage System (ESS) for Charging and Discharging Operations via Machine Learning” and the work presented in it are my own. I confirm that:  \n■ This work was done wholly or mainly while in candidature for a master’s degree at the University of Stavanger.  \n■ Where I have consulted the published work of others, this is always clearly attributed.  \n■ Where I have quoted from the work of others, the source is always given. With the exception of such quotations, this thesis is entirely my own work.  \n■ I have acknowledged all main sources of help.  \n“Be alone, that is the secret of invention; be alone, that is when ideas are born.”  \n– Nikola Tesla  \n’I1  \nAbstract  \nFigure 1: Graphical Abstract  \nPowerpal like other energy companies faces the problem of effective energy distribution to its clients. Currently Energy Storage System (ESS) deployed by Powerpal is being charged at midnight to its full capacity (325kWh) for next day operations. Following this method of charging does not guarantee optimal results in terms of efficiency and profit for Powerpal. The solution to this problem is the topic of research for this thesis paper.  \nThis thesis paper examines whether machine learning can enhance the efficiency and cost effectiveness of charging and discharging operations for ESS at Powerpal, comparing it to the current ESS model utilized by Powerpal. Powerpal has been keeping track of their operations by recording time series data. This provided an opportunity to deploy machine learning particularly Long short-term memory (LSTM) on their historical data. This machine learning model forecasted energy demand (from electric vehicles and Smedvigkvartalet) and energy generation (from solar and the electric power grid) facilitating informed decisions on ESS charging and discharging.  \nTwo separate LSTM methods i.e. Univariate and Multivariate were trained on dataset from Powerpal. In order for the comparison between these two models tobe fair, hyperparameters i.e., learning rate, sequence length, epochs, optimizer, hidden state size and activation function in dense layer were kept constant for the two models. The percentage change between the values predicted my Univariate in comparison to actual data was i.e., maximum difference of-9.2 % and minimum difference of-1.2 . Whereas in the case of Multivariate value prediction in comparison to actual data, the percentage difference was i.e., maximum difference of-47.2 % and minimum difference of-13.9 % . Univariate was better at making accurate prediction in comparison to Multivariate. After predicting the supply and demand of energy from both these models for April 2024, cost analysis was done i.e., which model resulted in more money saved by Powerpal for ESS operations. Because of more accurate predictions made by Univariate LSTM, Powerpal saved more money in comparison to Multivariate LSTM for its ESS operations for the month of April 2024 . With more accuracy in expected supply of energy from solar and grid predictions, the utilization of ESS for charging and discharging was less, hence decreasing the operational cost of running ESS, that is why Univariate resulted in more money saved for ESS operations.  \nThe results showed the potential of LSTM machine learning model in making predictions on Powerpal dataset. Powerpal can utilise these results to include machine learning in their daily operations for ESS.  \nAcknowledgements  \nI would like to express my sincere gratitude to everyone who supported me throughout the duration of this thesis. First and foremost, I am deeply thankful to my thesis supervisors, Enrico Riccardi from University of Stavanger and Rune Stangeland from Powerpal, for their ","cbCaibTFTJynbOxp","https://ap.wps.com/l/cbCaibTFTJynbOxp","pdf",1938741,1,41,"English","en",105,"# Abstract\n# Acknowledgements\n# Introduction\n## Background and Motivation\n## Objectives\n## Thesis Structure\n# Literature Review\n## Comparative analysis of ARIMA and LSTM for time series forecasting\n## Forecasting electric load using machine learning and statistical techniques\n## Application of statistical and deep learning methods on NASDAQ stock exchange dataset\n## Predictions of COVID-19 spread in Canada using LSTM\n## Forecasting hydrological time series during periods of low flow using LSTM\n## Forecasting time series data of web traffic using LSTM\n## Forecasting aquifer levels with LSTM neural networks\n## Machine learning algorithms for rainfall time series forecasting\n## Deep learning to predict air quality time series\n# Methodology","[{\"question\":\"What problem does the thesis address for Powerpal’s energy storage system (ESS)?\",\"answer\":\"Powerpal currently charges the ESS at midnight to full capacity, which does not guarantee optimal efficiency and profit for subsequent operations.\"},{\"question\":\"How does the thesis use machine learning to improve ESS operations?\",\"answer\":\"It applies LSTM models to Powerpal’s historical time-series data to forecast both energy demand and energy generation, enabling better decisions for charging and discharging.\"},{\"question\":\"Why did the thesis find univariate LSTM to perform better than multivariate LSTM?\",\"answer\":\"The univariate model produced smaller percentage differences between predicted and actual values, leading to more accurate supply and demand forecasts and higher cost savings in April 2024.\"}]","Optimizing Energy Storage System (ESS) for Charging and Discharging Operations via Machine Learning - 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