TY - GEN
T1 - Using Markov Switching Model for solar irradiance forecasting in remote microgrids
AU - Shakya, Ayush
AU - Michael, Semhar
AU - Saunders, Christopher
AU - Armstrong, Douglas
AU - Pandey, Prakash
AU - Chalise, Santosh
AU - Tonkoski, Reinaldo
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016
Y1 - 2016
N2 - In recent years, there has been rapid growth of Photovoltaic (PV) system integration into diesel-based remote microgrids to reduce the diesel fuel consumption. However, due to low correlation of PV power availability with the load as well as uncertainty and variability of the PV power, the benefits of the integration have not been achieved properly. A large energy reserve is required to compensate the fluctuation and improve reliability, which leads to increased operational cost. Solar irradiance forecasting helps to reduce the reserve requirement and improve the PV energy utilization. In this paper, a novel solar irradiance forecasting using Markov Switching Model is proposed for remote microgrids. This forecasting method uses locally available historical irradiance data of the microgrid location to predict day-ahead irradiance. The case study for validating this method for Brookings, SD resulted in Root Mean Square Error (RMSE) of 99.6 W/m2 for 2008 and 106.8 W/m2 for 2011.
AB - In recent years, there has been rapid growth of Photovoltaic (PV) system integration into diesel-based remote microgrids to reduce the diesel fuel consumption. However, due to low correlation of PV power availability with the load as well as uncertainty and variability of the PV power, the benefits of the integration have not been achieved properly. A large energy reserve is required to compensate the fluctuation and improve reliability, which leads to increased operational cost. Solar irradiance forecasting helps to reduce the reserve requirement and improve the PV energy utilization. In this paper, a novel solar irradiance forecasting using Markov Switching Model is proposed for remote microgrids. This forecasting method uses locally available historical irradiance data of the microgrid location to predict day-ahead irradiance. The case study for validating this method for Brookings, SD resulted in Root Mean Square Error (RMSE) of 99.6 W/m2 for 2008 and 106.8 W/m2 for 2011.
KW - Clear Sky Irradiance (CSI)
KW - Fourier basis function
KW - Markov Switching Model (MSM)
KW - Mean Absolute Percentage Error (MAPE)
KW - Root Mean Square Error (RMSE)
UR - https://www.scopus.com/pages/publications/85015425357
U2 - 10.1109/ECCE.2016.7855546
DO - 10.1109/ECCE.2016.7855546
M3 - Conference contribution
AN - SCOPUS:85015425357
T3 - ECCE 2016 - IEEE Energy Conversion Congress and Exposition, Proceedings
BT - ECCE 2016 - IEEE Energy Conversion Congress and Exposition, Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2016 IEEE Energy Conversion Congress and Exposition, ECCE 2016
Y2 - 18 September 2016 through 22 September 2016
ER -
