Day-Ahead Probabilistic Forecasting of Net-Load and Demand Response Potentials with High Penetration of Behind-the-Meter Solar-plus-Storage
- North Carolina State University, Raleigh, NC (United States)
- Purdue Univ., West Lafayette, IN (United States)
The goal of this project is to develop advanced methods for day-ahead net-load forecasting, by leveraging the state-of-the-art machine learning techniques. The developed models produce both point and probabilistic forecasts for a variety of use cases, and are versatile to work with different types of data sets. The innovation lies in the novel design of the architectures, leveraging the most recent advances in machine learning that have not been explored in power systems, accompanied by techniques in the broader artificial intelligence fields such as fuzzy systems. This project has achieved the following accomplishments: (1) preprocessing of over 10 data sets covering varying geographical regions, time horizons, and system levels, which form a robust foundation for training and evaluating forecasting models across a wide range of realistic grid scenarios; (2) development of an interactive web app that enables exploratory analysis of load and generation data, and supports better understanding of data trends, anomalies, and correlations, facilitating model development and stakeholder engagement; (3) implementation of over 10 benchmark models for point and probabilistic forecasting, which include a mix of conventional machine learning methods and state-of-the-art deep learning approaches, providing a comprehensive baseline for performance comparison and validation of the proposed models; (4) development of a fuzzy system based gradient boosting model, tailored for small (less than 3 years) data sets, which achieves a mean absolute percentage error (MAPE) of 4% for point forecasting and a 20% improvement in average pinball loss for probabilistic forecasting; (5) development of a Transformer (a state-of-the-art deep learning architecture) based neural network model, tailored for large (3 years or more) data sets, which achieves a MAPE of 2% for point forecasting and a 20% improvement in average pinball loss for probabilistic forecasting; (6) development of a methodology for quantifying DR potential, and extensions of the previous models for multi-target forecasting of net load and DR potential, which achieve a MAPE of 10% for DR potential.
- Research Organization:
- North Carolina State University, Raleigh, NC (United States)
- Sponsoring Organization:
- USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Solar Energy Technologies Office
- Contributing Organization:
- North Carolina Electric Membership Corporation; Dominion Energy
- DOE Contract Number:
- EE0009357
- OSTI ID:
- 2560048
- Report Number(s):
- DOE-NCSU--09357
- Country of Publication:
- United States
- Language:
- English
Similar Records
Probabilistic Short-Term Wind Forecasting Based on Pinball Loss Optimization
Probabilistic Short-Term Wind Forecasting Based on Pinball Loss Optimization: Preprint
VRN3P: Variational Recurrent Neural Network Based Net-Load Prediction under High Solar Penetration
Conference
·
Mon Aug 20 00:00:00 EDT 2018
·
OSTI ID:1476243
Probabilistic Short-Term Wind Forecasting Based on Pinball Loss Optimization: Preprint
Conference
·
Fri Jun 29 00:00:00 EDT 2018
·
OSTI ID:1459614
VRN3P: Variational Recurrent Neural Network Based Net-Load Prediction under High Solar Penetration
Technical Report
·
Wed Jan 07 19:00:00 EST 2026
·
OSTI ID:3012343