TY - GEN
T1 - Statistical analysis of power systems and application to load forecasting
AU - Loewenstern, Yakir
AU - Katzir, Liran
AU - Shmilovitz, Doron
N1 - Publisher Copyright:
© Copyright 2015 IEEE All rights reserved.
PY - 2014
Y1 - 2014
N2 - For many years, Load Forecasting (LF) has been an area of intense research. Most research has focused on short and long-term forecasting, with "short-term" generally meaning one hour in advance, to enable some power grid operations tasks. However, finer-grained prediction, at a resolution of minutes, can assist with other tasks, such as Power System State Estimation, and matching load to renewable energy generation in developing Smart Grids. To allow such ephemeral-term prediction with high accuracy, analysis of historical data sampled at high frequency is necessary. In this paper, we present statistical analysis based on three years' worth of real data obtained from the New York Independent System Operator (NYISO). The data is fine-grained, at a resolution of one sample per five minutes. The advantage of this data set is the ability to verify the applicability of our results to both large and small systems. The data and analysis presented in this paper can be used as a baseline for future LF and Smart Grid research.
AB - For many years, Load Forecasting (LF) has been an area of intense research. Most research has focused on short and long-term forecasting, with "short-term" generally meaning one hour in advance, to enable some power grid operations tasks. However, finer-grained prediction, at a resolution of minutes, can assist with other tasks, such as Power System State Estimation, and matching load to renewable energy generation in developing Smart Grids. To allow such ephemeral-term prediction with high accuracy, analysis of historical data sampled at high frequency is necessary. In this paper, we present statistical analysis based on three years' worth of real data obtained from the New York Independent System Operator (NYISO). The data is fine-grained, at a resolution of one sample per five minutes. The advantage of this data set is the ability to verify the applicability of our results to both large and small systems. The data and analysis presented in this paper can be used as a baseline for future LF and Smart Grid research.
KW - Load forecasting
KW - Load modeling
KW - Power systems
KW - Smart Grid
UR - https://www.scopus.com/pages/publications/84941236774
U2 - 10.1109/EEEI.2014.7005879
DO - 10.1109/EEEI.2014.7005879
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AN - SCOPUS:84941236774
T3 - 2014 IEEE 28th Convention of Electrical and Electronics Engineers in Israel, IEEEI 2014
BT - 2014 IEEE 28th Convention of Electrical and Electronics Engineers in Israel, IEEEI 2014
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2014 28th IEEE Convention of Electrical and Electronics Engineers in Israel, IEEEI 2014
Y2 - 3 December 2014 through 5 December 2014
ER -
