Behavioral and Predictive Analytics for Efficient Energy Consumption Management in Smart Grids

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One of the objectives of smart grid systems is to maximize the efficiency of energy consumption management programs, by engaging end-users as central players in smart grid technology. Although enormous effort has been undertaken to promote this objective, unfortunately, consumers are not a central consideration according to a recent study conducted by IndEco Strategic Consulting Inc. for Natural Resource Canada. The study concluded that smart grid technology by 2030 "will be limited by weakness in consumer engagement". We hypothesize that behavioral and predictive analytics can advance utilities' knowledge of how to partner with consumers. This is particularly promising given the large volume of consumption data produced by smart meters which provide unprecedented opportunities for utilities to understand the dynamics on both sides of the meter. On the consumption side, behavioral analytics techniques allow utilities to uncover energy usage preferences that can be integrated into smart grid technologies. On the operation side, predictive analytics techniques allow utilities to interact with consumers in near real-time to facilitate infrastructure planning based on an accurate forecast of load demand.******This research program focuses on behavioral and predictive analytics aspects pertaining to household smart meter data. Behavioral analytics as an approach for understanding energy consumption in households is relatively new. Predictive analytics is well studied for short and long-term load forecasting. However, very-short-term [VST] (latency of few seconds or minutes) predictions that focus on the immediate use of energy are required to engage consumers in near real-time smart grid planning applications. ******The long-term objective of this research program is to explore innovative behavioural and predictive analytics techniques that support utilities and consumers in adopting efficient energy management programs. Specific short-term objectives are as follows. ******(1) The development of behavioural analytics mechanisms and methods to analyze comprehensively household energy consumption data to promote efficient energy management programs better ***(2) The development of new predictive analytics techniques for VST energy predictions and the development of new strategies to evaluate the performance of these techniques ***(3) The development of innovative platform to integrate data analytics techniques with fewer resource constraints and the development of new privacy-preserving mechanisms that balance the trade-off between privacy concerns and the use of data******This research program will provide unique opportunities to train HQPs in topics considered highly in-demand by Canadian companies. Also, the developed technologies are critically important for Canadian utilities seeking to promote energy consumption management programs that benefit Canada's economy and environment.**