首页|Researcher from Malardalen University Reports on Findings in Machine Learning (D eriving Input Variables through Applied Machine Learning for Short-Term Electric Load Forecasting in Eskilstuna, Sweden)
Researcher from Malardalen University Reports on Findings in Machine Learning (D eriving Input Variables through Applied Machine Learning for Short-Term Electric Load Forecasting in Eskilstuna, Sweden)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Investigators publish new report on ar tificial intelligence. According to news originating from Vasteras, Sweden, by N ewsRx correspondents, research stated, "As the demand for electricity, electrifi cation, and renewable energy rises, accurate forecasting and flexible energy man agement become imperative." Financial supporters for this research include Indtech. Our news reporters obtained a quote from the research from Malardalen University : "Distribution network operators face capacity limits set by regional grids, ri sking economic penalties if exceeded. This study examined data-driven approaches of load forecasting to address these challenges on a city scale through a use c ase study of Eskilstuna, Sweden. Multiple Linear Regression was used to model el ectric load data, identifying key calendar and meteorological variables through a rolling origin validation process, using three years of historical data. Despi te its low cost, Multiple Linear Regression outperforms the more expensive non-l inear Light Gradient Boosting Machine, and both outperform the "weekly Naive" be nchmark with a relative Root Mean Square Errors of 32-34% and 39-4 0%, respectively. Best-practice hyperparameter settings were derive d, and they emphasize frequent re-training, maximizing the training data size, a nd setting a lag size larger than or equal to the forecast horizon for improved accuracy. Combining both models into an ensemble could the enhance accuracy."
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