2019 AUG 14 (NewsRx) -- By a News Reporter-Staff News Editor at South Korea Daily Report -- Current study results on Machine Learning have been published. According to news reporting from Seoul, South Korea, by NewsRx journalists, research stated, “Effective use of energy storage systems (ESS) is important to reduce unnecessary power consumption. In this paper, a day-ahead two-stage ESS-scheduling model based on the use of a machine learning technique for load prediction has been proposed for minimizing the operating cost of the energy system.”
Funders for this research include Korea Electric Power Corporation, Korea Institute of Energy Technology Evaluation and Planning (KETEP), Ministry of Trade, Industry and Energy (MOTIE) of the Republic of Korea.
The news correspondents obtained a quote from the research from Duksung Women’s University, “The proposed algorithm consists of two stages of ESS. In the first stage, ESS is used to minimize demand charges by reducing the peak load. Then, the remaining capacity is used to reduce energy charges through arbitrage trading, thereby minimizing the total operating cost. To achieve this purpose, accurate load prediction is required. Machine learning techniques are promising methods owing to the ability to improve forecasting performance. Among them, ensemble learning is a well-known machine learning method which helps to reduce variance and prevent overfitting of a model. To predict loads, we employed bootstrap aggregating (bagging) or random forest technique-based decision trees after Holt-Winters smoothing for trends. Our combined method can increase the prediction accuracy. In the simulation conducted, three combined prediction models were evaluated. The prediction task was performed using the R programming language.”
According to the news reporters, the research concluded: “The effectiveness of the proposed algorithm was verified by using Python’s PuLP library.”
For more information on this research see: Development of a Two-Stage ESS-Scheduling Model for Cost Minimization Using Machine Learning-Based Load Prediction Techniques. Processes, 2019;7(6):370. Processes can be contacted at: Mdpi, St Alban-Anlage 66, Ch-4052 Basel, Switzerland.
Our news journalists report that additional information may be obtained by contacting J. Kim, Duksung Women’s University, Dept. of Statistics, 33, Samyang Ro 144 Gil, Seoul 01369, South Korea. Additional authors for this research include M. Park and D. Won.
The direct object identifier (DOI) for that additional information is: https://doi.org/10.3390/pr7060370. This DOI is a link to an online electronic document that is either free or for purchase, and can be your direct source for a journal article and its citation.
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