Data-driven customer energy behavior characterization for distributed energy management

With the ever-growing concerns of environmental and climate concerns for energy consumption in our society, it is crucial to develop novel solutions that improve the efficient utilization of distributed energy resources for energy efficiency and demand response (DR). As such, there is a need to develop targeted energy programs, which not only meet the requirement of energy goals for a community but also take the energy use patterns of individual households into account. To this end, a sound understanding of the energy behavior of customers at the neighborhood level is needed, which requires operational analytics on the wealth of energy data from customers and devices.

In this dissertation, we focus on data-driven solutions for customer energy behavior characterization with applications to distributed energy management and flexibility provision. To do so, the following problems were studied: (1) how different customers can be segmented for DR events based on their energy-saving potential and balancing peak and off-peak demand, (2) what are the opportunities for extracting Time-of-Use of specific loads for automated DR applications from the whole-house energy data without in-situ training, and (3) how flexibility in customer demand adoption of renewable and distributed resources (e.g., solar panels, battery, and smart loads) can improve the demand-supply problem.

In the first study, a segmentation methodology form historical energy data of households is proposed to estimate the energy-saving potential for DR programs at a community level. The proposed approach characterizes certain attributes in time-series data such as frequency, consistency, and peak time usage. The empirical evaluation of real energy data of 400 households shows the successful ranking of different subsets of consumers according to their peak energy reduction potential for the DR event. Specifically, it was shown that the proposed approach could successfully identify the 20-30% of customers who could achieve 50-70% total possible demand reduction for DR. Furthermore, the rebound effect problem (creating undesired peak demand after a DR event) was studied, and it was shown that the proposed approach has the potential of identifying a subset of consumers (~5%-40% with specific loads like AC and electric vehicle) who contribute to balance the peak and off-peak demand. A projection on Austin, TX showed 16MWh reduction during a 2-h event can be achieved by a justified selection of 20% of residential customers.

In the second study, the feasibility of inferring time-of-use (ToU) operation of flexible loads for DR applications was investigated. Unlike several efforts that required considerable model parameter selection or training, we sought to infer ToU from machine learning models without in-situ training. As the first part of this study, the ToU inference from low-resolution 15-minute data (smart meter data) was investigated. A framework was introduced which leveraged the smart meter data from a set of neighbor buildings (equipped with plug meters) with similar energy use behavior for training. Through identifying similar buildings in energy use behavior, the machine learning classification models (including neural network, SVM, and random forest) were employed for inference of appliance ToU in buildings by accounting for resident behavior reflected in their energy load shapes from smart meter data. Investigation on electric vehicle (EV) and dryer for 10 buildings over 20 days showed an average F-score of 83% and 71%. As the second part of this study, the ToU inference from high-resolution data (60Hz) was investigated. A self-configuring framework, based on the concept of spectral clustering, was introduced that automatically extracts the appliance signature from historical data in the environment to avoid the problem of model parameter selection. Using the framework, appliance signatures are matched with new events in the electricity signal to identify the ToU of major loads. The results on ~1500 events showed an F-score of >80% for major loads like AC, washing machine, and dishwasher.

In the third study, the problem of demand-supply balance, in the presence of varying levels of small-scale distributed resources (solar panel, battery, and smart load) was investigated. The concept of load complementarity between consumers and prosumers for load balancing among a community of ~250 households was investigated. The impact of different scenarios such as varying levels of solar penetration, battery integration level, in addition to users' flexibility for balancing the supply and demand were quantitatively measured. It was shown that (1) even with 100% adoption of solar panels, the renewable supply cannot cover the demand of the network during afternoon times (e.g., after 3 pm), (2) integrating battery for individual households could improve the self-sufficiency by more than 15% during solar generation time, and (3) without any battery, smart loads are also capable of improving the self-sufficiency as an alternative, by providing ~60% of what commercial battery systems would offer.

The contribution of this dissertation is through introducing data-driven solutions/investigations for characterizing the energy behavior of households, which could increase the flexibility of the aggregate daily energy load profiles for a community. When combined, the findings of this research can serve to the field of utility-scale energy analytics for the integration of DR and improved reshaping of network energy profiles (i.e., mitigating the peaks and valleys in daily demand profiles).