TY - JOUR
T1 - Multiple Novel Decomposition Techniques for Time Series Forecasting
T2 - Application to Monthly Forecasting of Electricity Consumption in Pakistan
AU - Iftikhar, Hasnain
AU - Bibi, Nadeela
AU - Canas Rodrigues, Paulo
AU - López-Gonzales, Javier Linkolk
N1 - Publisher Copyright:
© 2023 by the authors.
PY - 2023/3
Y1 - 2023/3
N2 - In today’s modern world, monthly forecasts of electricity consumption are vital in planning the generation and distribution of energy utilities. However, the properties of these time series are so complex that they are difficult to model directly. Thus, this study provides a comprehensive analysis of forecasting monthly electricity consumption by comparing several decomposition techniques followed by various time series models. To this end, first, we decompose the electricity consumption time series into three new subseries: the long-term trend series, the seasonal series, and the stochastic series, using the three different proposed decomposition methods. Second, to forecast each subseries with various popular time series models, all their possible combinations are considered. Finally, the forecast results of each subseries are summed up to obtain the final forecast results. The proposed modeling and forecasting framework is applied to data on Pakistan’s monthly electricity consumption from January 1990 to June 2020. The one-month-ahead out-of-sample forecast results (descriptive, statistical test, and graphical analysis) for the considered data suggest that the proposed methodology gives a highly accurate and efficient gain. It is also shown that the proposed decomposition methods outperform the benchmark ones and increase the performance of final model forecasts. In addition, the final forecasting models produce the lowest mean error, performing significantly better than those reported in the literature. Finally, we believe that the framework proposed for modeling and forecasting can also be used to solve other forecasting problems in the real world that have similar features.
AB - In today’s modern world, monthly forecasts of electricity consumption are vital in planning the generation and distribution of energy utilities. However, the properties of these time series are so complex that they are difficult to model directly. Thus, this study provides a comprehensive analysis of forecasting monthly electricity consumption by comparing several decomposition techniques followed by various time series models. To this end, first, we decompose the electricity consumption time series into three new subseries: the long-term trend series, the seasonal series, and the stochastic series, using the three different proposed decomposition methods. Second, to forecast each subseries with various popular time series models, all their possible combinations are considered. Finally, the forecast results of each subseries are summed up to obtain the final forecast results. The proposed modeling and forecasting framework is applied to data on Pakistan’s monthly electricity consumption from January 1990 to June 2020. The one-month-ahead out-of-sample forecast results (descriptive, statistical test, and graphical analysis) for the considered data suggest that the proposed methodology gives a highly accurate and efficient gain. It is also shown that the proposed decomposition methods outperform the benchmark ones and increase the performance of final model forecasts. In addition, the final forecasting models produce the lowest mean error, performing significantly better than those reported in the literature. Finally, we believe that the framework proposed for modeling and forecasting can also be used to solve other forecasting problems in the real world that have similar features.
KW - decomposition methods
KW - electricity consumption
KW - monthly forecasting
KW - times series models
UR - http://www.scopus.com/inward/record.url?scp=85151432646&partnerID=8YFLogxK
U2 - 10.3390/en16062579
DO - 10.3390/en16062579
M3 - Article
AN - SCOPUS:85151432646
SN - 1996-1073
VL - 16
JO - Energies
JF - Energies
IS - 6
M1 - 2579
ER -