Generation Forecasting for Small Run-of-the-River Hydroelectric Systems Using Statistical Learning Models

Thesis

Title: Generation Forecasting for Small Run-of-the-River Hydroelectric Systems Using Statistical Learning Models

Author:

Song, N.

Publication Date:

January 1, 2018

Thesis Type:

Master’s Thesis

Academic Department:

Electrical Engineering

Pages:

Affiliation:

University of Washington

Technology:

Current, Riverine

Collection Method:

Modeling

Document Access

Website:

External Link

Citation

Song, N. (2018). Generation Forecasting for Small Run-of-the-River Hydroelectric Systems Using Statistical Learning Models (Master’s Thesis). University of Washington.

Abstract

With an increasing interest in constructing new run-of-the-river (ROR) hydroelectric generation over the more traditional reservoir-based hydroelectric systems, there is an increasing operational challenge due to the volatility of streamflow. The Snohomish County Public Utilities District (SnoPUD) has recently invested in the construction and operation of 3 new run-of-the-river projects in Northwestern Washington along Calligan Creek, Hancock Creek, and Youngs Creek. In order to effectively plan generation dispatch, SnoPUD has expressed interest in the development of an accurate forecasting tool to predict the generation capacity for these ROR systems. The following research project aims to use statistical learning models, namely Hidden Markov Models (HMMs), to predict day-ahead generation capacities for the aforementioned ROR systems. These models are constructed using 12 years of historical streamflow data collected at the intake sites and precipitation data recorded at the National Oceanic and Atmospheric Administration (NOAA) Alpine Meadows station. Four methods of constructing the models are studied for their forecast accuracies, and are compared with the persistence model. Despite using only one set of observable variables, the HMMs are shown to have slight improvements in accuracy over the persistence model approach, which shows great optimism for future work.