Mixed data-driven decision-making in demand response management: An empirical evidence from dynamic time-warping based nonparametric-matching DID

As an important approach for demand-side management in the power sector, demand responses (DRs) are increasingly important in guiding scientific energy consumption behaviour. However, most related prior studies are based on small-scale experimental or survey data with a rule-based optimization algorithm; scientific DR management and strategy formulation studies driven by large-scale, hybrid frequency data are rare. This paper integrates a large-scale controlled trial, 15 min high-frequency power consumption data, and individual residents' monthly low-frequency power consumption data on a micro scale. The data-driven and causal analysis methods are combined and a machine-learning algorithm have been adopted to propose a dynamic time-warping (DTW) clustering-based difference-in-differences (DID) method. This non-parametric matching method successfully results in an intra-group randomized experiment. Empirical results reveal that cash-incentive-based DR can effectively stimulate electricity-saving behaviour, and families from the treatment groups save an average of 27.3% of their total electricity consumption in the experimental period. Further, a dynamic response process analysis indicates that a substantial discrepancy exists in the degree of demand response and the response modes of residents with different power consumption patterns. More importantly, prior empirical studies proved this method's effectiveness and f easibility: based on the DTW non-parametric matching method, the control and treatment groups can well support the parallel trend hypothesis. This work provides important implications for the accurate, efficient implementation and scientific decision-making of subsequent DR programs. (c) 2020 Elsevier Ltd. All rights reserved.

Automated summary

Demand responses (DRs) are increasingly important in guiding scientific energy consumption behavior. This paper proposes a dynamic time-warping (DTW) clustering-based difference-in-differences (DID) method for analyzing power consumption data, which can successfully stimulate electricity-saving behavior.