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Title: A Solar Time-based Analog Ensemble Method for Regional Solar Power Forecasting

Journal Article · · IEEE Transactions on Sustainable Energy
 [1];  [2];  [3];  [3];  [4];  [5]
  1. Northeastern Univ., Malden, MA (United States)
  2. Sichuan Univ., Chengdu (China)
  3. IBM, Yorktown Heights, NY (United States). Thomas J. Watson Research Center
  4. National Renewable Energy Lab. (NREL), Golden, CO (United States)
  5. Northeastern Univ., Boston, MA (United States)
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This paper presents a new analog ensemble method for day-ahead regional photovoltaic (PV) power forecasting with hourly resolution. By utilizing open weather forecast and power measurement data, this prediction method is processed within a set of historical data with similar meteorological data (temperature and irradiance), and astronomical date (solar time and earth declination angle). Further, clustering and blending strategies are applied to improve its accuracy in regional PV forecasting. The robustness of the proposed method is demonstrated with three different numerical weather prediction models, the North American Mesoscale Forecast System, the Global Forecast System, and the Short-Range Ensemble Forecast, for both region level and single site level PV forecasts. Using real measured data, the new forecasting approach is applied to the load zone in Southeastern Massachusetts as a case study. In conclusion, the normalized root mean square error (NRMSE) has been reduced by 13.80%-61.21% when compared with three tested baselines.

Research Organization:
National Renewable Energy Lab. (NREL), Golden, CO (United States)
Sponsoring Organization:
USDOE Office of Energy Efficiency and Renewable Energy (EERE)
Grant/Contract Number:
AC36-08GO28308
OSTI ID:
1439549
Report Number(s):
NREL/JA-5D00-71656
Journal Information:
IEEE Transactions on Sustainable Energy, Vol. 10, Issue 1; ISSN 1949-3029
Publisher:
IEEECopyright Statement
Country of Publication:
United States
Language:
English
Citation Metrics:
Cited by: 90 works
Citation information provided by
Web of Science

Cited By (6)

Short‐term photovoltaic power dynamic weighted combination forecasting based on least squares method journal September 2019
A Framework of Using Machine Learning Approaches for Short-Term Solar Power Forecasting journal January 2020
Forecasting of PV plant output using hybrid wavelet-based LSTM-DNN structure model journal May 2019
Big data solar power forecasting based on deep learning and multiple data sources journal March 2019
Advanced Methods for Photovoltaic Output Power Forecasting: A Review journal January 2020
A Novel Ensemble Algorithm for Solar Power Forecasting Based on Kernel Density Estimation journal January 2020

Figures / Tables (16)

Fig. 1(p. 3)figure Fig. 1
Fig. 2(p. 4)figure Fig. 2
Table I(p. 4)table Table I
Fig. 3(p. 5)figure Fig. 3
Fig. 4(p. 6)figure Fig. 4
Fig. 5(p. 7)figure Fig. 5
Table II(p. 7)table Table II
Table III(p. 7)table Table III
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Table IV(p. 8)table Table IV
Fig. 7(p. 9)figure Fig. 7
Fig. 8(p. 9)figure Fig. 8
Table V(p. 9)table Table V
Fig. 10(p. 10)figure Fig. 10
Fig. 9(p. 10)figure Fig. 9
Table VI(p. 10)table Table VI

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14 SOLAR ENERGY
solar power forecasting
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analog
ensemble
modeling