[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128622-en":3,"doc-seo-128622-105":30,"detail-sidebar-cat-0-en-105":92},{"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},128622,962084925636,"Olivia Brown","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","Nord Pool Day-ahead Electiricty Price Forecasting using machine learning","Electricity differs from other commodities because forecasting depends on understanding the precise equilibrium between supply and demand. Since Norway’s Energy Act in 1991, the electricity market has evolved into a regulated, interconnected marketplace, shaping how bids and offers are formed using expected time-ahead conditions. Renewable generation introduces uncertainty and higher volatility, so suppliers rely on price predictions to improve profit outcomes. This thesis builds robust, accurate day-ahead electricity price forecasting models that produce estimates aligned with real-time values, supporting better stakeholder decisions.","Faculty of Economics and Social Sciences  \nMeaza G. Ghebretsadik  \nCandidate nr.118  \nMaster thesis in Digital Management and Business Analysis.  \nNord pool Day-ahead Electiricty Price Forecasting using  \nmachine learning.  \nMay 2024  \nAcknowledgements  \nThis thesis was conducted at Innlandet Norway University of Applied Scines, Campus Kongsvinger, within the main profile of Digital management and business development, focusing on Business Analysis.Guidance and support throughout this were provided by my main supervisor, Pooja Joshi. Working on this thesis has been both the most challenging and rewarding experience. Balancing the responsibilities of being a full-time worker and a mother was inherently demanding, and adding education to the mix presented its own set of challenges. I am deeply grateful to my supervisor, Pooja Joshi, for her invaluable assistance in overcoming obstacles, particularly in data management, feature extraction, and machine learning issues.  \nA special expression of gratitude is owed to my husband for his unwavering patience, uplifting words, and constant support in managing household responsibilities while I focused on my research. Without him, navigating this journey would have been far more challenging. Additionally, I extend my heartfelt thanks to my children for their understanding and patience during times when I was preoccupied with my studies, even when it meant being away on weekends. Their understanding and support have been instrumental in my progress.  \n02.05. 2024, Hamar  \nMeaza G. Ghebretsadik  \nAbstract  \nElectricity stands apart from other commodities due to its unique characteristics, forecasting requires a deep understanding of precise equilibrium between supply and demand. Since the enactment of the Energy Act in 1991, Norway's electricity market has undergone profound transformations, which eventually results in a regulated and interconnected marketplace. One of the notable impacts is the evolution of market bid and offers based on expected time-ahead supply and demand. In this scenario, suppliers strategically craft bidding plans, leveraging electricity price predictions in order to optimize profits. Note that, with the advent of renewable energy sources (such as solar, wind, hydroelectric), suppliers are witnessing significant uncertainties and heightened volatility in this space. Therefore, the primary objective of this thesis is to develop state-of-the-art electricity price forecasting models that boast robustness and accuracy, delivering estimates closely aligned with realtime values. This will result in improved decision making, and eventually improve economic gains for stakeholders involved in the electricity sector.  \nWhile numerous statistical studies have been proposed in the past, their estimation capabilities have generally been limited. This is largely due to the highly non-linear and uncertain nature of exogenous variables within electricity markets. Recently, Deep Learning-based approaches have gained significant attention for their ability to efficiently estimate functions, even in scenarios involving highly non-linear exogenous variables and complex data spaces. Therefore, inspired by the proven success of Deep Learning in navigating highly non-linear data spaces, this thesis attempts to investigate its applicability in energy price forecasting. It rigorously follows the established practices of Machine Learning, including data preprocessing, model training, and analysis, using the NordPool energy market dataset as a primary source of examination. A range of Deep Learning methodologies were explored, including Feedforward Neural Networks, Artificial Neural Networks (ANN), Deep Neural Networks (DNN), Recurrent Neural Networks (RNN), Convolutional Neural Networks (CNN), Gated Recurrent Units (GRU), and Long Short-Term Memory (LSTM) networks. As an innovative aspect of this research, various dropout techniques and parameters were also investigated. The results indicate","cbCaisVxkPyKRtU3","https://ap.wps.com/l/cbCaisVxkPyKRtU3","pdf",3554397,1,83,"English","en",105,"# Introduction\n## Background and Motivation\n## Problem Description and Research Gaps\n## Approach\n## Thesis Structure\n# Nord pool electricity market\n## History of the electricity market in Nord Pool\n## Electricity price calculation","[{\"question\":\"Why is electricity price forecasting uniquely challenging compared to other commodities?\",\"answer\":\"Electricity forecasting requires maintaining an accurate balance between supply and demand. Market dynamics and external variables introduce strong non-linearity and uncertainty.\"},{\"question\":\"What is the main goal of the thesis?\",\"answer\":\"Develop state-of-the-art day-ahead electricity price forecasting models that are robust and accurate, producing estimates close to real-time values.\"},{\"question\":\"Which deep learning models achieved the best results?\",\"answer\":\"The ANN model delivered the highest performance, followed by LSTM and RNN. Dropout techniques and related parameters were also investigated.\"}]","Nord Pool Day-ahead Electiricty Price Forecasting using machine learning | PDF",1786002157,209,{"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":87,"head_meta":89,"extra_data":91,"updated_unix":28},"nord-pool-day-ahead-electiricty-price-forecasting-using-machine-learning","",{"@graph":36,"@context":86},[37,54,69],{"@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/nord-pool-day-ahead-electiricty-price-forecasting-using-machine-learning/128622/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-23","2026-08-06",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"Why is electricity price forecasting uniquely challenging compared to other commodities?","Question",{"text":76,"@type":77},"Electricity forecasting requires maintaining an accurate balance between supply and demand. Market dynamics and external variables introduce strong non-linearity and uncertainty.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"What is the main goal of the thesis?",{"text":81,"@type":77},"Develop state-of-the-art day-ahead electricity price forecasting models that are robust and accurate, producing estimates close to real-time values.",{"name":83,"@type":74,"acceptedAnswer":84},"Which deep learning models achieved the best results?",{"text":85,"@type":77},"The ANN model delivered the highest performance, followed by LSTM and RNN. 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