title: ÐÑÐ¾Ð³Ð½Ð¾Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ðµ ÑÐµÐ½ Ð½Ð° ÑÐ»ÐµÐºÑÑÐ¾ÑÐ½ÐµÑÐ³Ð¸Ñ Ñ Ð¿ÑÐ¸Ð¼ÐµÐ½ÐµÐ½Ð¸ÐµÐ¼ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð¾Ð² Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð±Ð°ÐºÐ°Ð»Ð°Ð²ÑÐ°
titleType: Subtitle
title: Predicting electricity prices using machine learning algorithms
Creators: name: ÐÑÐ°Ð½Ð°ÑÐµÐ²Ð¸Ñ, ÐÐ»Ð°Ð´Ð¸ÑÐ»Ð°Ð²
Publisher: name: Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³ÑÐºÐ¸Ð¹ Ð¿Ð¾Ð»Ð¸ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¹ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑ ÐÐµÑÑÐ° ÐÐµÐ»Ð¸ÐºÐ¾Ð³Ð¾
Year: 2021
Subjects: ÑÑÐ½Ð¾Ðº Ð½Ð° ÑÑÑÐºÐ¸ Ð²Ð¿ÐµÑÐµÐ´
Ð¿ÑÐ¾Ð³Ð½Ð¾Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ðµ
Ð¿ÑÐµÐ´ÑÐºÐ°Ð·Ð°Ð½Ð¸Ðµ
ÑÐµÐ½Ñ Ð½Ð° ÑÐ»ÐµÐºÑÑÐ¾ÑÐ½ÐµÑÐ³Ð¸Ñ
Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ
Ð³ÑÐ°Ð´Ð¸ÐµÐ½ÑÐ½ÑÐ¹ Ð±ÑÑÑÐ¸Ð½Ð³
day-ahead market
forecasting
prediction
price or electricity
machine learning
gradient boosting

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description: The given work is devoted to studying possibilities and advantages of forecasting prices on the wholesale electricity market in Russia for consumers of the day-ahead market. The research set the following goals: 1. Analyzing the structures of the electricity market in the Russian Federation. 2. Features of the day-ahead market (DAM) functioning. 3. Forecasting the prices of electric energy on the DAM by an econometric method. 4. Description of the data specifics and applying a machine learning method to predict electricity prices. 5. A practical example for the use of gradient boosting as a specific machine-learning algorithm. 6. Evaluation of the quality for the models built during the work and their comparison with each other. 7. Evaluation of the economic efficiency if using a machine-learning model. The work was fulfilled on data from key organizations, due to which competitive conditions for electricity pricing are possible in the Russian Federation. Trading System Administrator (TSA) JSC and the non-profit partnership Market Council association are organizations, from where was taken most of the necessary factual data about the organization of market relations in the wholesale electricity market, rules and peculiarities of participation in the day-ahead market (DAM), as well as daily indices and volumes from DAM. The study resulted into a machine-learning model based on the prebuilt gradient boosting algorithm, and then it was evaluated in comparison with the econometric method of multiple regression and autoregression. For the model with the best values of metrics of the quality for predicted prices, an evaluation was also made for the economic efficiency of its implementation at the consumerâs enterprise as the participant role of the wholesale electricity market.