Harvesting-aware energy management for environmental monitoring WSN

Wireless sensor networks can be used to collect data in remote locations, especially when energy harvesting is used to extend the lifetime of individual nodes. However, in order to use the collected energy most effectively, its consumption must be managed. In this work, forecasts of diurnal solar energies were made based on measurements of atmospheric pressure. These forecasts were used as part of an adaptive duty cycling scheme for node level energy management. This management was realized with a fuzzy logic controller that has been tuned using differential evolution. Controllers were created using one and two days of energy forecasts, then simulated in software. These controllers outperformed a human-created reference controller by taking more measurements while using less reserve energy during the simulated period. The energy forecasts were comparable to other available methods, while the method of tuning the fuzzy controller improved overall node performance. The combination of the two is a promising method of energy management.Web of Science105art. no. 60

Adaptive measurements of urban runoff quality

An approach to adaptively measure runoff water quality dynamics is introduced, focusing specifically on characterizing the timing and magnitude of urban pollutographs. Rather than relying on a static schedule or flow‐weighted sampling, which can miss important water quality dynamics if parameterized inadequately, novel Internet‐enabled sensor nodes are used to autonomously adapt their measurement frequency to real‐time weather forecasts and hydrologic conditions. This dynamic approach has the potential to significantly improve the use of constrained experimental resources, such as automated grab samplers, which continue to provide a strong alternative to sampling water quality dynamics when in situ sensors are not available. Compared to conventional flow‐weighted or time‐weighted sampling schemes, which rely on preset thresholds, a major benefit of the approach is the ability to dynamically adapt to features of an underlying hydrologic signal. A 28 km2 urban watershed was studied to characterize concentrations of total suspended solids (TSS) and total phosphorus. Water quality samples were autonomously triggered in response to features in the underlying hydrograph and real‐time weather forecasts. The study watershed did not exhibit a strong first flush and intraevent concentration variability was driven by flow acceleration, wherein the largest loadings of TSS and total phosphorus corresponded with the steepest rising limbs of the storm hydrograph. The scalability of the proposed method is discussed in the context of larger sensor network deployments, as well the potential to improving control of urban water quality.Key PointsAn Internet‐enabled sensor node autonomously adapts to weather forecasts and hydrograph features to collect water quality samplesFirst flush was not observed and peak loadings were primarily driven by erosion and flashinessCompared to present methods, our framework significantly reduces manpower and resource requirements in the study of water quality dynamicsPeer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/135503/1/wrcr22370.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/135503/2/wrcr22370_am.pd

Optimization of building performance via model-based predictive control

Il controllo predittivo basato su modello (MPC) è una tecnica di controllo avanzata che ha svolto un ruolo importante nella gestione di molti processi nel settore industriale. Oggi, nell’ottica di una gestione energetica efficiente degli edifici, l’utilizzo di questa strategia si sta dimostrando una soluzione promettente per ridurre al minimo i consumi e i costi energetici complessivi. Tuttavia, gli studi sulla sua fattibilità tecnica in edifici esistenti sono ancora in una fase iniziale. Pertanto, il risultato principale di questa tesi è la progettazione e lo sviluppo di un prototipo hardware e software per la verifica sul campo di un sistema di controllo predittivo, basato su modello, integrando un modello predittivo virtuale della porzione dell'edificio in esame, il controllore e l'interfaccia grafica per i dispositivi di monitoraggio e regolazione utilizzati. Inoltre, particolare attenzione è stata posta sulla fattibilità tecnica relativa all'implementazione di un tipico sistema MPC, che include un sottosistema di monitoraggio, un set di acquisizione dati e un metodo di identificazione del sistema per ottenere il modello per il controllore, mediante un approccio di modellazione grey-box. La fase di modellazione e l'approccio empirico sviluppato sono presentati nella prima parte di questa tesi di ricerca, mentre la parte centrale riguarda: lo sviluppo del prototipo di controllo predittivo, basato su modello, all'interno di uno strumento virtuale del software LabVIEW e la descrizione del test sperimentale, effettuato durante la stagione di riscaldamento, garantendo la normale operatività dell’edificio durante l'intero periodo di monitoraggio. Infine, è presentato lo studio sviluppato in ambiente di simulazione per indagare il potenziale della logica di controllo per la valutazione di scenari di riqualificazione. Il focus è sulla definizione dei principali componenti del simulatore MPC e sui risultati ottenuti testando uno degli scenari di intervento.Model Predictive Control (MPC) is an advanced control technique which has played an important role in the management of many processes in the industry sector. Nowadays, in the perspective of an efficient building energy management, the exploitation of this strategy is proving to be a promising solution for minimising overall energy consumptions and costs. However, investigations on the feasibility of the technique in real existing buildings are at an initial stage. Hence, the main outcome of this dissertation is the design and development of a prototype hardware and software set up for on-field testing of a model-based predictive control system, integrating a virtual predictive model of the portion of the building under investigation, the controller and the interface to the monitoring and regulation devices used. Moreover, this research is addressed to investigate the technical feasibility of the development and deployment of a typical MPC system, which includes a monitoring sub-system, a data acquisition set up and a system identification method to obtain the model for the controller by means of a grey-box modelling approach. The modelling phase and the empirical approach developed are presented in the first part of this research thesis, while the core part concerns: the development of the MPC prototype, within a virtual instrument of LabVIEW software and the description of the experimental test, which was carried out during heating season, ensuring normal building operation during the entire monitoring period. Finally, this dissertation presents the study developed in simulation environment to investigate the potential of the control logic for the evaluation of retrofitting scenarios. The focus is on the definition of the main MPC simulator components and on the results obtained by testing one of the intervention scenarios

Model predictive energy control of ventilation for underground stations

Smart building systems are opening up new markets, nevertheless the implementation of these novel technologies still lacks suitable and proven whole engineering solutions in complex buildings. This paper presents a detailed approach for the ventilation control of an underground space, as an example of application of the developed solution to a very harsh environment but also highly demanding in terms of energy consumption. The underground spaces are characterized by a particular thermal behavior, because of the continuous and huge thermal exchange they have with the outside, via the openings and the ground surrounding the majority of the building. The main objective of the developed methodology is to reduce energy consumption of ventilation control while maintaining acceptable comfort levels: succeeding in achieving this twofold goal in a real station and the generalization of the approach are the most relevant contributions of the paper. The developed solution is based on a Model-based Predictive Control algorithm used together with a proper monitoring platform. The model predictive control is based on a Bayesian environmental prediction model, which works in cooperation with a weather forecast web service, schedule-based predictions about trains and external fans and an occupancy detection system to appraise the real amount of people. The prediction model develops scenarios useful to allow the controller acting in advance in order to adapt the system to the current and future conditions of use, taking profit of the knowledge of the real ventilation demand. Finally, the proposed control architecture is applied to the Passeig de Gràcia metro station in Barcelona as a case study, validating the usefulness of the proposed approach and obtaining more than 30% of energy savings in the ventilation system, while maintaining the pre-existing comfort levels. The saving percentage values estimated by simulation are confirmed by the direct measures continuously taken on site through energy-meters

wireless real time monitoring system for the implementation of intelligent control in subways

This chapter looks into the technical features of state-of-the-art wireless sensors networks for environmental monitoring. Technology advances in low-power and wireless devices have made the deployment of those networks more and more affordable. In addition, wireless sensor networks have become more flexible and adaptable to a wide range of situations. Hence, a framework for their correct implementation will be provided. Then, one specific application about real-time environmental monitoring in support of a modelbased predictive control system installed in a metro station will be described. In these applications, filtering, resampling, and post-processing functions must be developed, in order to convert raw data into a dataset arranged in the right format, so that it can inform the algorithms of the control system about the current state of the domain under control. Finally, the whole architecture of the model-based predictive control and its final performances will be reported

Adaptive-predictive control strategy for HVAC systems in smart buildings – A review

Abstract High share of energy consumption in buildings and subsequent increase in greenhouse gas emissions along with stricter legislations have motivated researchers to look for sustainable solutions in order to reduce energy consumption by using alternative renewable energy resources and improving the efficiency in this sector. Today, the smart building and socially resilient city concepts have been introduced where building automation technologies are implemented to manage and control the energy generation/consumption/storage. Building automation and control systems can be roughly classified into traditional and advanced control strategies. Traditional strategies are not a viable choice for more sophisticated features required in smart buildings. The main focus of this paper is to review advanced control strategies and their impact on buildings and technical systems with respect to energy/cost saving. These strategies should be predictive/responsive/adaptive against weather, user, grid and thermal mass. In this context, special attention is paid to model predictive control and adaptive control strategies. Although model predictive control is the most common type used in buildings, it is not well suited for systems consisting of uncertainties and unpredictable data. Thus, adaptive predictive control strategies are being developed to address these shortcomings. Despite great progress in this field, the quantified results of these strategies reported in literature showed a high level of inconsistency. This is due to the application of different control modes, various boundary conditions, hypotheses, fields of application, and type of energy consumption in different studies. Thus, this review assesses the implementations and configurations of advanced control solutions and highlights research gaps in this field that need further investigations