To be presented at IEEE Rural Electric Power Conference 2014 on May 19th in Fort Worth, Texas. The paper is a brief summary of several of my projects with cooperatives. The full paper is available on IEEE Xplore. The working paper version is available HERE.
Citation
Tao Hong, Tom Laing and Pu Wang, "Four Best Practices of Load Forecasting for Electric Cooperatives", 2014 IEEE Rural Electric Power Conference, Fort Worth, Texas, May 18 - 21, 2014
Abstract
Several characteristics of electric cooperatives, such as large territories with varied climate, small customer density, high granularity load data, complex forecasting requirements and a small forecasting team, bring both opportunities and challenges to their load forecasting practices. This paper discusses four best practices from the electric cooperative sector using case studies from North Carolina Electric Membership Corporation (NCEMC), one of the largest Generation and Transmission Cooperatives in the nation. These best practices include taking advantage of hierarchical weather and load information to enhance forecasting accuracy, deploying an integrated load forecasting methodology to do more with less, and developing scenario based forecasts to mitigate risk.
Citation
Tao Hong, Tom Laing and Pu Wang, "Four Best Practices of Load Forecasting for Electric Cooperatives", 2014 IEEE Rural Electric Power Conference, Fort Worth, Texas, May 18 - 21, 2014
Four Best Practices of Load Forecasting for Electric Cooperatives
Tao Hong, Tom Laing and Pu Wang
Abstract
Several characteristics of electric cooperatives, such as large territories with varied climate, small customer density, high granularity load data, complex forecasting requirements and a small forecasting team, bring both opportunities and challenges to their load forecasting practices. This paper discusses four best practices from the electric cooperative sector using case studies from North Carolina Electric Membership Corporation (NCEMC), one of the largest Generation and Transmission Cooperatives in the nation. These best practices include taking advantage of hierarchical weather and load information to enhance forecasting accuracy, deploying an integrated load forecasting methodology to do more with less, and developing scenario based forecasts to mitigate risk.
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