Renewable power generation price prediction and forecasting using machine learning

Rao, Challa Krishna; Sahoo, Sarat Kumar; Yanine, Fernando

Renewable power generation price prediction and forecasting using machine learning

dc.contributor.author	Rao, Challa Krishna
dc.contributor.author	Sahoo, Sarat Kumar
dc.contributor.author	Yanine, Fernando
dc.coverage.spatial	USA
dc.date.accessioned	2024-12-03T15:42:46Z
dc.date.available	2024-12-03T15:42:46Z
dc.date.issued	2024-05-28
dc.description.abstract	In the power market, electricity price forecasting (EPF) is a crucial consideration for decision-making. The need to optimize earnings by adjusting bids in day-ahead power markets is becoming more and more important to different market participants. Prior information is required for marketers to have an advantage over the competition while controlling the risk of pricing fluctuation. However, not all marketers must accurately predict the worth of future pricing when making decisions. To make a choice, it is necessary to determine whether the cost will be prohibitive. Thus, in order to determine the prices that have an impact on marketers, electricity price classification is first performed. Based on a threshold value, prices are categorized as low class prices and high class pricing. In order to determine the precise value of prices for utility maximization, the EPF has been explored next. In order to maximize advantage or utility, buying and selling bidding techniques rely on the accurate projections of prices for the following day. Effective forecasting models can improve the performance of producers and consumers, who play important roles in the electrical markets. The EPF approaches now in use produce complicated models and are not generalizable.The best method for forecasting prices with superior generalization performance, kernel functions, and distributive prediction is to use machine learning (ML)-based models. In order to forecast prices, machine learning-based electricity price forecasting has been studied. On the markets of Ontario, Austria, and India, the ML models have been put to the test. Neural networks, support vector machines, core vector machines, information vector machines, and relevance vector machines (RVM) have all been used with EPF, and their performances have been assessed in order to determine the optimum model. When all of these models are examined, it becomes clear that RVM is the most effective and trustworthy ML approach for various markets. The price of power fluctuates a lot, with both typical costs and spikes that might be tens to hundreds of times higher than the average price range. The prediction for the overall price of energy is created by adding together average prices and price peaks. A good and precise price spike forecast is required because price spikes have a big influence on the electrical market. As a result, a threshold value has also been used to forecast the spike value. The potential for more research in this field has also been emphasized.
dc.identifier.citation	En: Sivaraman, P.; Padmanaban, Sanjeevikumar; Sharmeela, C. Microgrids for Commercial Systems: Design, Installation, and Operation. Wiley, 2023. pp. 21-47.
dc.identifier.doi	https://doi.org/10.1002/9781394167319.ch2
dc.identifier.isbn	9781394167319
dc.identifier.orcid	https://orcid.org/0000-0003-1086-0840
dc.identifier.uri	http://hdl.handle.net/20.500.12254/3961
dc.language.iso	en
dc.publisher	Wiley
dc.rights	Atribución-NoComercial-CompartirIgual 3.0 Chile (CC BY-NC-SA 3.0 CL)
dc.rights.uri	http://creativecommons.org/licenses/by-nc-sa/3.0/cl/
dc.subject	Microgrids
dc.subject	Smart grid
dc.subject	Renewable power generation
dc.subject	Machine learning
dc.title	Renewable power generation price prediction and forecasting using machine learning
dc.type	Book chapter