Wireless sensors and IoT platform for intelligent HVAC control

Energy consumption of buildings (residential and non-residential) represents approximately 40% of total world electricity consumption, with half of this energy consumed by HVAC systems. Model-Based Predictive Control (MBPC) is perhaps the technique most often proposed for HVAC control, since it offers an enormous potential for energy savings. Despite the large number of papers on this topic during the last few years, there are only a few reported applications of the use of MBPC for existing buildings, under normal occupancy conditions and, to the best of our knowledge, no commercial solution yet. A marketable solution has been recently presented by the authors, coined the IMBPC HVAC system. This paper describes the design, prototyping and validation of two components of this integrated system, the Self-Powered Wireless Sensors and the IOT platform developed. Results for the use of IMBPC in a real building under normal occupation demonstrate savings in the electricity bill while maintaining thermal comfort during the whole occupation schedule.QREN SIDT [38798]; Portuguese Foundation for Science & Technology, through IDMEC, under LAETA [ID/EMS/50022/2013

Final Causality in the Thought of Thomas Aquinas

Throughout his corpus, Thomas Aquinas develops an account of final causality that is both philosophically nuanced and interesting. The aim of my dissertation is to provide a systematic reconstruction of this account of final causality, one that clarifies its motivation and appeal. The body of my dissertation consists of four chapters. In Chapter 1, I examine the metaphysical underpinnings of Aquinas’s account of final causality by focusing on how Aquinas understands the causality of the final cause. I argue that Aquinas holds that an end is a cause because it is the determinate effect toward which an agent’s action is directed. I proceed by first presenting the general framework of causality within which Aquinas understands final causality. I then consider how Aquinas justifies the reality of each of the four kinds of cause, placing special emphasis on the final cause. In Chapter 2, I consider final causality from the perspective of goodness and explore the reasons why Aquinas thinks that the end of an action is always good. For even if one was convinced that the end of an action is indeed a cause, one might still resist attributing any normative or evaluative properties to the end, much less a positively-valenced normative property like goodness. In this chapter, I show how, given Aquinas’s metaphysics of powers and his characterization of goodness as that which all desire, it follows that every action is for the sake of some good. In Chapter 3, I consider Aquinas’s account of the relation between final causality and cognition. In many passages throughout his corpus—most famously in the fifth of his Five Ways—Aquinas advances the claim that cognition plays an essential role in final causality. In this chapter, I explore Aquinas’s account of the relation between final causality and cognition by reconstructing his Fifth Way and investigating the metaphysical foundations on which it rests. While the first three chapters of my dissertation focus on Aquinas’s account of final causality from the perspective of the ends of individual agents, in Chapter 4 I broaden my focus to consider the way in which the account of final causality developed in these earlier chapters shapes Aquinas’s philosophical cosmology. I argue that, on Aquinas’s view, when an individual agent acts for an end, it is plays a role in a larger system, e.g. a polis, an ecosystem, or the universe itself

Model Predictive Control of HVAC Systems: Design and Implementation on a Real Case Study

The final aim of this work is to design, implement and test a controller on a real testbed kindly provided by KTH. The control paradigm presented in this thesis is a MPC that aims at saving energy as well as keeping the temperature and the CO2 concentration in a comfort range that guarantees the wellness of room occupants. To improve the knowledge of the plant, we also study the problem of modeling both the dynamics of of the system to be controlled and of the dedicated actuation syste

An investigation into the energy and control implications of adaptive comfort in a modern office building

PhD ThesisAn investigation into the potentials of adaptive comfort in an office building is carried out using fine grained primary data and computer modelling. A comprehensive literature review and background study into energy and comfort aspects of building management provides the backdrop against which a target building is subjected to energy and comfort audit, virtual simulation and impact assessment of adaptive comfort standard (BS EN 15251: 2007). Building fabric design is also brought into focus by examining 2006 and 2010 Approved Document part L potentials against Passive House design. This is to reflect the general direction of regulatory development which tends toward zero carbon design by the end of this decade. In finishing a study of modern controls in buildings is carried out to assess the strongest contenders that next generation heating, ventilation and air-conditioning technologies will come to rely on in future buildings. An actual target building constitutes the vehicle for the work described above. A virtual model of this building was calibrated against an extensive set of actual data using version control method. The results were improved to surpass ASHRAE Guide 14. A set of different scenarios were constructed to account for improved fabric design as well as historical weather files and future weather predictions. These scenarios enabled a comparative study to investigate the effect of BS EN 15251:2007 when compared to conventional space controls. The main finding is that modern commercial buildings built to the latest UK statutory regulations can achieve considerable carbon savings through adaptive comfort standard. However these savings are only modestly improved if fabric design is enhanced to passive house levels. Adaptive comfort can also be readily deployed using current web-enabled control applications. However an actual field study is necessary to provide invaluable insight into occupants’ acceptance of this standard since winter-time space temperature results derived from BS EN 15251:2007 constitute a notable departure from CIBSE environmental guidelines

CONSIDERATIONS CONCERNING THE AUTOMATION OF PROTECTED SPACES

In the last period there is an intensification of the researches oriented towards the automation of the specific activities of the horticultural production in protected spaces. The greenhouses offer a shelter in which a microclimate suitable for plants is maintained, which is obtained by regulating / adjusting the heat and the amount of light coming from the sun, by means of actuation systems (actuators-technical devices that generate an action to reach a specific objective). The paper presents a brief communication on the main drive systems used in greenhouses: ventilation and cooling systems; heating systems; irrigation systems, whose drive systems are mainly composed of electrical devices, especially electric motors or pump

Online Learning Algorithm for Time Series Forecasting Suitable for Low Cost Wireless Sensor Networks Nodes

Time series forecasting is an important predictive methodology which can be applied to a wide range of problems. Particularly, forecasting the indoor temperature permits an improved utilization of the HVAC (Heating, Ventilating and Air Conditioning) systems in a home and thus a better energy efficiency. With such purpose the paper describes how to implement an Artificial Neural Network (ANN) algorithm in a low cost system-on-chip to develop an autonomous intelligent wireless sensor network. The present paper uses a Wireless Sensor Networks (WSN) to monitor and forecast the indoor temperature in a smart home, based on low resources and cost microcontroller technology as the 8051MCU. An on-line learning approach, based on Back-Propagation (BP) algorithm for ANNs, has been developed for real-time time series learning. It performs the model training with every new data that arrive to the system, without saving enormous quantities of data to create a historical database as usual, i.e., without previous knowledge. Consequently to validate the approach a simulation study through a Bayesian baseline model have been tested in order to compare with a database of a real application aiming to see the performance and accuracy. The core of the paper is a new algorithm, based on the BP one, which has been described in detail, and the challenge was how to implement a computational demanding algorithm in a simple architecture with very few hardware resources.Comment: 28 pages, Published 21 April 2015 at MDPI's journal "Sensors