• Energy Research

In this paper, we present a formal analysis and description of the steady-state behavior of an electrostatic vibration energy harvester operating in constant-charge mode and using different types of electromechanical transducers. The method predicts parameter values required to start oscillations, allows a study of the dynamics of the transient process, and provides a rigorous description of the system, necessary for further investigation of the related nonlinear phenomena and for the optimisation of converted power. We show how the system can be presented as a nonlinear oscillator and be analysed by the multiple scales method, a type of perturbation technique. We analyse two the most common cases of the transducer geometry and find the amplitude and the phase of steady-state oscillations as functions of parameters. The analytical predictions are shown to be in good agreement with the results obtained by behavioral modeling.

This paper presents a comprehensive electromechanical study of an electrostatic kinetic energy harvester (eKEH) biased with a continuous voltage. This simple circuit refers to any capacitive transducer with an in-built voltage source, such as an electret, or DC biased transducer connected directly to a resistive load. It is commonly used for the characterisation of experimental transducers. However there is a void of any unified discussion focusing on the circuits influence on the transducer, due to electromechanical coupling in the transducer. The electromechanical coupling causes numerous nonlinear effects, any of which can result in inconsistent results and disjointed conclusions of experimental devices. This article seeks to present analytic tools which can be employed for systems of this type and discusses some of the results obtained from these techniques. Along with analysis and characterisation of the circuit dynamics, the results are compared with an experimental device and presented in terms of design of the system and conclusions drawn relating to the optimum operation point.

Abstract We investigated clustering techniques on time series of daily electric load profiles of fourteen higher education buildings on the same campus. A k-means algorithm is implemented, and three different methods are compared: time-series features extraction with Manhattan distance and raw time series with Euclidian distance and Dynamic Time Warping. The impact of data characteristics with data collection time-steps and timeframes is studied using a database of more than 6,500 daily electric load profiles. We show that Euclidian distance applied to electric demand time series with three-month timeframes and ten-minute time-step provides the most consistent clustering results. In addition, useful insights are highlighted for non-residential buildings electric demand modeling and forecasting. Two groups of buildings can be distinguished regarding electric load profile patterns. On one hand, teaching, research, libraries, and gymnasium buildings show similar patterns distributed in two clusters corresponding to business days and closing days load profiles. On the other hand, campus office buildings present a larger number of clusters inconsistent with day-type dependent load profiles. A seasonal effect is also observed using six-month and one-year timeframes. Finally, a two-cluster distribution is obtained when aggregating all buildings load profiles.

In this study, a medical grade pulse rate (PR) instrument was used to monitor hemodialysis patients, and the wearable product was applied for the 4 h observation. Electrocardiogram (ECG) and photoplethysmography (PPG) data were simultaneously collected to observe physiological phenomena in patients undergoing hemodialysis. The analyzed results of 38 patients undergoing the treatment (as sympathetic/parasympathetic balance indicators before-hemodialysis (HD), and after-HD) and autonomic nerve activation for the pulse rate (PR) measurement accompanied by squeezing a soft ball were also observed. The results prove the pulse rate measurement while squeezing the soft ball and analyze data, and we show that the analyzed results have a very concentrated normal distribution. This study presents oxygen saturation (SpO2) and continuous pulse rate distribution curves during the 4 h observation of the hemodialysis patients and we show that some patients undergoing kidney dialysis have sleep apnea. They become lethargic during dialysis and experience severe hypoxia due to intermittent respiratory arrest. Studies have confirmed that such monitoring and biofeedback designs can reduce the incidence of hypotension during dialysis.

Based on the formal analysis of a resonant electrostatic vibration energy harvester operating in constant-charge mode with a gap-closing transducer, we show that the system displays universal behaviour patterns. In this paper, we treat the harvester as a nonlinear forced oscillator and bound the area of control parameters where the system displays regular harmonic oscillations allowing the conditioning circuit to operate in the most effective mode. Before the system exhibits irregular behaviour, there exists a universal optimal value of normalised converted power regardless of the system design and control parameters.

Enhancing residential buildings energy efficiency has become a critical goal to take up current challenges of human comfort, urbanization growth and the consequent energy consumption increase. In a context of integrated smart infrastructures, sensor networks offer a relevant solution to support building energy consumption monitoring, operation and prediction. The amount of accessible data with such networks also opens new prospects to better consider key parameters such as human behaviour and to lead to more efficient energy retrofit of existing buildings. However, sensor networks planning and implementation in general, and in existing buildings in particular, is a particularly complex task facing many challenges and affecting the performances of such a promising solution. In the present paper, we report on a field experiment of a sensor network deployment involving more than 250 sensors in three collective residential buildings in Paris region for the evaluation of a deep energy retrofit. More specifically, we describe the whole process of the sensor network design and roll-out and highlight the main critical aspects in such complex process. We also provide a feedback after several months of the sensor network operation and preliminary analysis of collected data. Reported results path the way for an efficient and optimized design and deployment of sensor networks for energy and indoor environment quality monitoring in existing buildings.