國立虎尾科技大學 |

Machine Learning Ensembles for Grid Congestion Price Forecasting.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Machine Learning Ensembles for Grid Congestion Price Forecasting./
作者:	Javed, Asim.
面頁冊數:	1 online resource (70 pages)
附註:	Source: Masters Abstracts International, Volume: 84-12.
Contained By:	Masters Abstracts International84-12.
標題:	Computer science. -
電子資源:	click for full text (PQDT)
ISBN:	9798379714475

Machine Learning Ensembles for Grid Congestion Price Forecasting.
Javed, Asim.

Machine Learning Ensembles for Grid Congestion Price Forecasting. - 1 online resource (70 pages)

Source: Masters Abstracts International, Volume: 84-12.

Thesis (M.S.)--University of Missouri - Kansas City, 2023.

Includes bibliographical references

In this thesis, we embarked on a comprehensive study to develop a cutting-edge model for forecasting real-time electricity prices across 35 nodes within the PJM zone. The task at hand was particularly challenging, given the volatility of the day-ahead electricity market and the numerous factors that influence prices, such as load variations, weather conditions, and historical prices. Our objective was to devise a model that could provide more accurate day-ahead price forecasts than existing methods. To achieve this goal, we proposed an ensemble-based approach that leveraged the strengths of low-bias and high-variance machine learning models. To handle missing values, we employed K-Nearest Neighbors (KNN) imputation. To enhance the performance of the models, we employed Principal Component Analysis (PCA) and correlation feature selection techniques. We then employed a direct multi-output strategy to forecast real-time prices. Our ensemble incorporated a variety of models such as Support Vector Regression (SVR), Huber Regression, and deep neural networks such as Convolutional Neural Network (CNN), Long-Short Term Memory (LSTM), Bidirectional LSTM (BiLSTM), and Temporal Convolutional Network (TCN). Our results on test data from the first half of 2021 demonstrate that our proposed strategy outperforms any single model by 8.75% over all 35 nodes and beats the day-ahead prices. However, we noticed a decrease in testing accuracy in the latter half of 2021, indicating a need for a more dynamic ensemble fusion. In conclusion, our research provides valuable insights into electricity price forecasting and illustrates the effectiveness of ensemble learning techniques, incremental learning, and deep neural networks for time series forecasting. Our proposed method can be utilized by energy traders, independent system operators, and policymakers to make more informed decisions in the uncertain and volatile energy market.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798379714475Subjects--Topical Terms:

573171
Computer science.
Subjects--Index Terms:

Deep neural networksIndex Terms--Genre/Form:

554714
Electronic books.

Machine Learning Ensembles for Grid Congestion Price Forecasting.
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504 $a Includes bibliographical references
520 $a In this thesis, we embarked on a comprehensive study to develop a cutting-edge model for forecasting real-time electricity prices across 35 nodes within the PJM zone. The task at hand was particularly challenging, given the volatility of the day-ahead electricity market and the numerous factors that influence prices, such as load variations, weather conditions, and historical prices. Our objective was to devise a model that could provide more accurate day-ahead price forecasts than existing methods. To achieve this goal, we proposed an ensemble-based approach that leveraged the strengths of low-bias and high-variance machine learning models. To handle missing values, we employed K-Nearest Neighbors (KNN) imputation. To enhance the performance of the models, we employed Principal Component Analysis (PCA) and correlation feature selection techniques. We then employed a direct multi-output strategy to forecast real-time prices. Our ensemble incorporated a variety of models such as Support Vector Regression (SVR), Huber Regression, and deep neural networks such as Convolutional Neural Network (CNN), Long-Short Term Memory (LSTM), Bidirectional LSTM (BiLSTM), and Temporal Convolutional Network (TCN). Our results on test data from the first half of 2021 demonstrate that our proposed strategy outperforms any single model by 8.75% over all 35 nodes and beats the day-ahead prices. However, we noticed a decrease in testing accuracy in the latter half of 2021, indicating a need for a more dynamic ensemble fusion. In conclusion, our research provides valuable insights into electricity price forecasting and illustrates the effectiveness of ensemble learning techniques, incremental learning, and deep neural networks for time series forecasting. Our proposed method can be utilized by energy traders, independent system operators, and policymakers to make more informed decisions in the uncertain and volatile energy market.
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