Cross-Temporal Hierarchical Forecast Reconciliation of Natural Gas Demand

Colin O. Quinn, George F. Corliss and Richard J. Povinelli ()
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Colin O. Quinn: Department of Computer Science, Marquette University, 1313 W. Wisconsin Ave, Milwaukee, WI 53233, USA
George F. Corliss: Department of Electrical and Computer Engineering, Marquette University, 1515 W. Wisconsin Ave, Milwaukee, WI 53233, USA
Richard J. Povinelli: Department of Electrical and Computer Engineering, Marquette University, 1515 W. Wisconsin Ave, Milwaukee, WI 53233, USA

Energies, 2024, vol. 17, issue 13, 1-18

Abstract: Local natural gas distribution companies (LDCs) require accurate demand forecasts across various time periods, geographic regions, and customer class hierarchies. Achieving coherent forecasts across these hierarchies is challenging but crucial for optimal decision making, resource allocation, and operational efficiency. This work introduces a method that structures the gas distribution system into cross-temporal hierarchies to produce accurate and coherent forecasts. We apply our method to a case study involving three operational regions, forecasting at different geographical levels and analyzing both hourly and daily frequencies. Trained on five years of data and tested on one year, our model achieves a 10% reduction in hourly mean absolute scaled error and a 3% reduction in daily mean absolute scaled error.

Keywords: hierarchical time-series forecasting; cross-temporal forecast reconciliation; natural gas demand; spatial and geographical coherent forecasts (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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