Simulating Hydropower Discharge using Multiple Decision Tree Methods and a Dynamical Model Merging Technique
Publication: Journal of Water Resources Planning and Management
Volume 146, Issue 2
Abstract
Hydropower release decision making relies on multisource information, such as climate conditions, downstream water quality, inflow and storage, regulation and engineering constraints, and so on. The decision tree (DT) method is one of the commonly used techniques to simulate reservoir operation and release strategies because of its simplicity and effectiveness. However, the performances and simulation accuracy vary among different DT models due to many structures and splitting rules associated with each DT model. In this study, we propose a dynamic merge technique (DMerge), which adopts a concept from particle swarm optimization, to postprocess outputs from different DT models with the purpose of increasing the simulation accuracy and producing a model ensemble with dynamically changing weights throughout the validation phase. A case study of Shasta Lake in northern California is presented, where the daily hydropower releases are predicted and compared using the DMerge, AdaBoost DT, random forest, and extremely randomized trees methods. Results show that the DMerge method has the best statistics compared to other popular DT algorithms. Furthermore, scenario tests were carried out to analyze the sensitivity to model inputs (i.e., hydrological condition, reservoir storage and regulation, climate phenomenon indices, and water quality) with respect to explaining the variability of hydropower releases. According to the results, we found that the hydropower releases are a complex decision-making process and water quality and climate conditions could play an even more significant role than both hydrological forcing and system states in our case study. The proposed DMerge method is a robust and efficient technique in solving water-energy prediction and simulation problems, and it is suitable for joint use with other data-driven approaches.
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Data Availability Statement
Some or all data, models, or code generated or used during the study are available in a repository or online from the California Data Exchange Center (http://cdec.water.ca.gov/index.html) and the NOAA Earth System Research Laboratory (http://www.esrl.noaa.gov/psd/data/climateindices/list/). The digital elevation model raster files are obtained from http://www.webgis.com/srtm3.html for the State of California. Information about the Journal’s data-sharing policy can be found here: https://ascelibrary.org/doi/10.1061/(ASCE)CO.1943-7862.0001263.
Acknowledgments
The financial support for this study is from the DOE (Prime Award No. DE-IA0000018), CEC (Award No. 300-15-005), the Open Foundation of Key Laboratory of Soil and Water Loss Process and Control on the Loess Plateau of Ministry of Water Resources, and a NASA Minority University Research and Education Project (MUREP) Institutional Research Opportunity grant (NNX15AQ06A).
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Information & Authors
Information
Published In
Journal of Water Resources Planning and Management
Volume 146 • Issue 2 • February 2020
Copyright
©2019 American Society of Civil Engineers.
History
Received: Nov 15, 2018
Accepted: May 22, 2019
Published online: Nov 30, 2019
Published in print: Feb 1, 2020
Discussion open until: Apr 30, 2020
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