首页|Study Data from Norwegian University of Science and Technology (NTNU) Update Knowledge of Machine Learning (Electricity Demand Forecasting With Hybrid Classical Statistical and Machine Learning Algorithms: Case Study of Ukraine)
Study Data from Norwegian University of Science and Technology (NTNU) Update Knowledge of Machine Learning (Electricity Demand Forecasting With Hybrid Classical Statistical and Machine Learning Algorithms: Case Study of Ukraine)
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NETL
NSTL
Elsevier
Data detailed on Machine Learning have been presented. According to news reporting originating in Trondheim, Norway, by NewsRx journalists, research stated, “This article presents a novel hybrid approach using classic statistics and machine learning to forecast the national demand of electricity. As investment and operation of future energy systems require long-term electricity demand forecasts with hourly resolution, our mathematical model fills a gap in energy forecasting.” Financial support for this research came from Joachim Herz Foundation. The news reporters obtained a quote from the research from the Norwegian University of Science and Technology (NTNU), “The proposed methodology was constructed using hourly data from Ukraine’s electricity consumption ranging from 2013 to 2020. To this end, we analysed the underlying structure of the hourly, daily and yearly time series of electricity consumption. The long-term yearly trend is evaluated using macroeconomic regression analysis. The mid-term model integrates temperature and calendar regressors to describe the underlying structure, and combines ARIMA and LSTM ‘black-box’pattern-based approaches to describe the error term. The short-term model captures the hourly seasonality through calendar regressors and multiple ARMA models for the residual. Results show that the best forecasting model is composed by combining multiple regression models and a LSTM hybrid model for residual prediction. Our hybrid model is very effective at forecasting long-term electricity consumption on an hourly resolution.”
TrondheimNorwayEuropeAlgorithmsCyborgsEmerging TechnologiesMachine LearningNorwegian University of Science and Technology (NTNU)
NETL
NSTL
Elsevier
