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Net Load Forecasting With Disaggregated Behind-the-Meter PV Generation

Journal Article · · IEEE Transactions on Industry Applications
 [1];  [2];  [3];  [4]
  1. Univ. of Nebraska, Lincoln, NE (United States)
  2. Argonne National Laboratory (ANL), Argonne, IL (United States)
  3. Rensselaer Polytechnic Inst., Troy, NY (United States)
  4. Eaton Corporation, Cleveland, OH (United States)
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As worldwide use of residential photovoltaic (PV) systems grows, system operators and utilities will need to transition from forecasting pure demand to forecasting net load with behind-the-meter (BTM) PV generation. However, PV generation can be difficult to predict and the measurements of PV generation from BTM residential systems are often invisible behind a measurement of the net load, making net load forecasting challenging. This paper proposes a novel two-stage framework for net load forecasting in areas with limited observability and high BTM PV generation. First, the profiles of observable customers are used to disaggregate the net load measurements into the pure load and PV generation. Then, separate models are used to forecast the PV generation and pure load individually, and the results are combined for a net load forecast. Further, this paper also proposes a compensator for correcting the error of the net load forecast, using historical forecast errors of the PV generation, pure load, and net load. The proposed framework is tested through two case studies for areas with high BTM PV penetration and less than 10% observable customers. The two-stage forecasting model is compared to two benchmark methods - a time series forecasting model, and a model that forecasts the net load directly using historical net load measurements. Results show that the proposed disaggregation-forecasting framework reduces the error of the net load forecast compared to both benchmark models. In addition, when the net load forecast error is periodic, the compensator can correct the error to improve the forecast accuracy.
Research Organization:
Argonne National Laboratory (ANL), Argonne, IL (United States)
Sponsoring Organization:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Solar Energy Technologies Office
Grant/Contract Number:
AC02-06CH11357
OSTI ID:
2375798
Journal Information:
IEEE Transactions on Industry Applications, Journal Name: IEEE Transactions on Industry Applications Journal Issue: 5 Vol. 59; ISSN 0093-9994
Publisher:
IEEECopyright Statement
Country of Publication:
United States
Language:
English

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Related Subjects

14 SOLAR ENERGY
24 POWER TRANSMISSION AND DISTRIBUTION
PV generation
additives
dictionaries
disaggregation
forecasting
load forecasting
load modeling
net load
predictive models
time series analysis