TY - GEN
T1 - New Short Term Load Forecasting models based on growth rate scaling and simple averaging
AU - Sreekumar, Sreenu
AU - Verma, Jatin
AU - Sujil, A.
AU - Kumar, Rajesh
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/10/5
Y1 - 2016/10/5
N2 - Load Forecasting is the link joining the strategic power system operation with the almighty mathematic algorithms in order to bring greater reliability, efficiency and economy in the system. This paper brings two novel forecasting engines, named SYGRSA and SYGRSAWP, for Short Term Load Forecasting (STLF), i.e., load forecasting with time leads ranging from one day to one week. STLF itself plays a crucial role in the control and scheduling operations of a power system. Modern techniques have been used to improve the accuracy of existing load prediction models using proper feature selection and consideration of necessary factors. The proposed models combine similar day approach, growth rate scaling and averaging techniques. SYGRSAWP is an optimised form of SYGRSA. Both the models have the potential to handle large historical data in short period of time. Moreover, they show remarkable forecasting accuracy. Further, a comparison between the two models has been analysed.
AB - Load Forecasting is the link joining the strategic power system operation with the almighty mathematic algorithms in order to bring greater reliability, efficiency and economy in the system. This paper brings two novel forecasting engines, named SYGRSA and SYGRSAWP, for Short Term Load Forecasting (STLF), i.e., load forecasting with time leads ranging from one day to one week. STLF itself plays a crucial role in the control and scheduling operations of a power system. Modern techniques have been used to improve the accuracy of existing load prediction models using proper feature selection and consideration of necessary factors. The proposed models combine similar day approach, growth rate scaling and averaging techniques. SYGRSAWP is an optimised form of SYGRSA. Both the models have the potential to handle large historical data in short period of time. Moreover, they show remarkable forecasting accuracy. Further, a comparison between the two models has been analysed.
KW - Short Term Load Forecasting
KW - Similar day Yearly Growth Rate Scaled Averaging (SYGRSA) Model
KW - Similar day Yearly Growth Rate Scaled Averaging With Previous days (SYGRSAWP) Model
UR - http://www.scopus.com/inward/record.url?scp=84994086915&partnerID=8YFLogxK
U2 - 10.1109/ICPES.2016.7584177
DO - 10.1109/ICPES.2016.7584177
M3 - Conference contribution
AN - SCOPUS:84994086915
T3 - 2016 IEEE 6th International Conference on Power Systems, ICPS 2016
BT - 2016 IEEE 6th International Conference on Power Systems, ICPS 2016
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 6th IEEE International Conference on Power Systems, ICPS 2016
Y2 - 4 March 2016 through 6 March 2016
ER -