Sustainable energy consumption monitoring in residential settings

Abstract

Abstract-The continuous growth of energy needs and the fact that unpredictable energy demand is mostly served by unsustainable (i.e. fossil-fuel) power generators have given rise to the development of Demand Response (DR) mechanisms for flattening energy demand. Building effective DR mechanisms and user awareness on power consumption can significantly benefit from fine-grained monitoring of user consumption at the appliance level. However, installing and maintaining such a monitoring infrastructure in residential settings can be quite expensive. In this paper, we study the problem of fine-grained appliance power-consumption monitoring based on one houselevel meter and few plug-level meters. We explore the trade-off between monitoring accuracy and cost, and exhaustively find the minimum subset of plug-level meters that maximize accuracy. As exhaustive search is time-and resource-consuming, we define a heuristic approach that finds the optimal set of plug-level meters without utilizing any other sets of plug-level meters. Based on experiments with real data, we found that few plug-level meterswhen appropriately placed -can very accurately disaggregate the total real power consumption of a residential setting and verified the effectiveness of our heuristic approach.

Appliance and state recognition using Hidden Markov Models

“…Other works propose more complex variants of HMMs. Factorial HMM (FHMM) [1][6] [7][10] [15] seems quite popular in NILM context. FHMM allows a single observation to be related with the hidden variables of multiple independent Markov Chains.…”

An IoT-Based Gamified Approach for Reducing Occupants’ Energy Wastage in Public Buildings

“…Non-Intrusive Appliance Load Monitoring approaches (NIALM) have been investigated [4,5], but only for plug-level loads, however, the problem in general involves a trade-off between installing costly dense metering infrastructure and accuracy [6]. Our approach advances the state of the art in energy disaggregation in two ways: (i) all energy loads in the building are monitored and disaggregated at the device level in a cost-effective and highly accurate way; (ii) device usage is associated to specific individual(s) in the building; note that in public buildings multiple appliances are shared among multiple users and thus personalizing energy use is a key non-trivial contribution of our approach.…”