Multi-sensor IoT devices often rely on renewable energy and batteries to support diverse field deployments. A device’s sensors use significant energy, so careful energy management is needed to maximize its operating time while meeting application requirements. Model Predictive Control (MPC), a successful energy management technique for other application domains, requires significant computational resources usually not available in typical IoT sensing devices. We develop and evaluate a low-complexity approximation to MPC for IoT device operations powered by renewable (solar) energy, where the approximation is guided by the charging characteristics of real batteries. The complex MPC optimization problem is solved by decomposing it into a time-dependent energy allocation problem and a task-dependent sensor scheduling problem, each of which is solvable with cubic time complexity. We utilize a novel combination of an incremental max-min fair allocation method and a recursive dynamic programming-like procedure. This low-complexity predictive optimization step is integrated with a very simple parabola-based adaptive solar prediction to provide a full system solution, termed Predictive EneRgy Management for IoT (PERMIT) , that can be implemented in lightweight IoT devices. PERMIT is evaluated through experiments on a solar-powered Raspberry Pi device with multiple sensors. We also complement our evaluations with simulations based on real device data traces. Results show that PERMIT’s low-complexity algorithm approximates the exact MPC solution very closely. PERMIT also performs significantly better than Signpost, a comparable IoT energy management solution.

Commercial buildings are significant consumers of electricity. We propose a number of methods for managing power in commercial buildings. The first step toward better energy management in commercial buildings is monitoring consumption. However, instrumenting every electrical panel in a large commercial building is an expensive proposition. In this article, we demonstrate that it is also unnecessary. Specifically, we propose a greedy meter (sensor) placement algorithm based on maximization of information gain subject to a cost constraint. The algorithm provides a near-optimal solution guarantee, and our empirical results demonstrate a 15% improvement in prediction power over conventional methods. Next, to identify power-saving opportunities, we use an unsupervised anomaly detection technique based on a low-dimensional embedding. Furthermore, to enable a building manager to effectively plan for demand response programs, we evaluate several solutions for fine-grained, short-term load forecasting. Our investigation reveals that support vector regression and an ensemble model work best overall. Finally, to better manage resources such as lighting and HVAC, we propose a semisupervised approach combining hidden Markov models (HMMs) and a standard classifier to model occupancy based on readily available port-level network statistics. We show that the proposed two-step approach simplifies the occupancy model while achieving good accuracy. The experimental results demonstrate an average occupancy estimation error of 9.3% with a potential reduction of 9.5% in lighting load using our occupancy models.

Commercial buildings are significant consumers of electricity. The first step towards better energy management in commercial buildings is monitoring consumption. However, instrumenting every electrical panel in a large commercial building is expensive and wasteful. In this paper, we propose a greedy meter (sensor) placement algorithm based on maximization of information gained, subject to a cost constraint. The algorithm provides a near-optimal solution guarantee. Furthermore, to identify power saving opportunities, we use an unsupervised anomaly detection technique based on a low-dimensional embedding. Further, to better manage resources such as lighting and HVAC, we propose a semi-supervised approach combining hidden Markov models (HMM) and a standard classifier to model occupancy based on readily available port-level network statistics.

Fear of increasing prices and concern about climate change are motivating residential power conservation efforts. We investigate the effectiveness of several unsupervised disaggregation methods on low frequency power measurements collected in real homes. Specifically, we consider variants of the factorial hidden Markov model. Our results indicate that a conditional factorial hidden semi-Markov model, which integrates additional features related to when and how appliances are used in the home and more accurately represents the power use of individual appliances, outperforms the other unsupervised disaggregation methods. Our results show that unsupervised techniques can provide perappliance power usage information in a non-invasive manner, which is ideal for enabling power conservation efforts.

Asset management in state of the art data centers is still a manual process. An automated system to track the location of servers and create a real-time inventory would not only improve operations management but also reduce operational costs. This demo shows a novel application of location aware wireless networks, RFID technology and visualization software integrated in end-to-end system to track servers in a data center.