Understanding and predicting the electric consumption patterns in the short-, mid- and long-term, at the distribution and transmission level, is a fundamental asset for smart grids infrastructure planning, dynamic network reconfiguration, dynamic energy pricing and savings, and thus energy efficiency. This work introduces the Functional Principal Component Analysis (FPCA) as a versatile method to both investigate and predict, at different level of spatial aggregation, the consumption patterns. The method was applied to a unique and sensitive dataset that includes electric consumption and contractual information of Milan metropolitan area. The decomposition of the load patterns into principal functions was found to be a powerful method to identify the physical and behavioral causes underlying the daily consumptions, given knowledge of exogenous variables such as calendar and meteorological data. The effectiveness of long-term predictions based on principal functions was proved on Milan's metropolitan area data and assessed on a publicly-available dataset.

Functional principal component analysis as a versatile technique to understand and predict the electric consumption patterns

Grillo S.;
2020

Abstract

Understanding and predicting the electric consumption patterns in the short-, mid- and long-term, at the distribution and transmission level, is a fundamental asset for smart grids infrastructure planning, dynamic network reconfiguration, dynamic energy pricing and savings, and thus energy efficiency. This work introduces the Functional Principal Component Analysis (FPCA) as a versatile method to both investigate and predict, at different level of spatial aggregation, the consumption patterns. The method was applied to a unique and sensitive dataset that includes electric consumption and contractual information of Milan metropolitan area. The decomposition of the load patterns into principal functions was found to be a powerful method to identify the physical and behavioral causes underlying the daily consumptions, given knowledge of exogenous variables such as calendar and meteorological data. The effectiveness of long-term predictions based on principal functions was proved on Milan's metropolitan area data and assessed on a publicly-available dataset.
Analysis; Electric consumption; FPCA; Functional principal component analysis; Patterns; Prediction
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1136790
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