Advanced control strategies toward achieving nearly-zero energy consumption in buildings

In this paper the main concept and results of the PEBBLE Project are presented: PEBBLE is an ongoing FP7 Project aiming at the development of advanced ICT tools to support the operation of nearly-zero- and positive energy buildings. In the design and operation of such buildings a pragmatic target is maximization of the actual net energy produced (NEP) by intelligently shaping demand to perform generation-consumption matching. With the belief that maximization of the NEP for Positive-Energy Buildings is attained thru Better ControL decisions (PEBBLE), a control and optimization ICT methodology that combines model-based predictive control and cognitive-based adaptive optimization is presented. There are three essential ingredients to the PEBBLE system: a) thermal simulation models; b) sensors, actuators, and user interfaces; and c), generic control and optimization tools. The potential for energy savings using advanced control strategies is illustrated using simulation-based studies: there are significant benefits in terms of energy-performance of using advanced control strategies, compared to traditional rule-based ones. Ongoing work about demonstration and evaluation of the PEBBLE system in three real world buildings is described

Εξελιγμένες Στρατηγικές Ελέγχου για την Επίτευξη Μηδενικής Ενεργειακής Κατανάλωσης σε Κτήρια [ = Developing control strategies toward nearly-zero energy buildings]

Στην παρούσα εργασία παρουσιάζονται η κεντρική ιδέα και τα αποτελέσματα του προγράμματος PEBBLE: το PEBBLE είναι ένα πρόγραμμα το οποίο στοχεύει στην ανάπτυξη εξελιγμένων Τεχνολογιών Πληροφορικής και Επικοινωνιών, οι οποίες θα υποστηρίξουν τη λειτουργία Κτηρίων Θετικού ή Μηδενικού Ισοζυγίου. Στο σχεδιασμό και τη λειτουργία τέτοιων κτηρίων ρεαλιστικό στόχο αποτελεί η μεγιστοποίηση της Καθαρής Παραγόμενης Ενέργειας, μέσω ευφυούς διαμόρφωσης της ζήτησης, ώστε να επιτευχθεί σύγκλιση παραγωγής-κατανάλωσης. Με την πεποίθηση ότι η μεγιστοποίηση της Καθαρής Παραγόμενης Ενέργειας για Κτήρια Θετικού Ισοζυγίου επιτυγχάνεται με τη λήψη καλύτερων αποφάσεων ελέγχου, παρουσιάζεται μια μεθοδολογία ελέγχου και βελτιστοποίησης, η οποία συνδυάζει τεχνικές Προβλεπτικού Ελέγχου βασισμένου σε Μοντέλα και Προσαρμοστικής Βελτιστοποίησης. Στο σύστημα PEBBLE συνυπάρχουν τρία βασικά συστατικά: α) μοντέλα θερμικής προσομοίωσης, β) αισθητήρες, επενεργητές, και διεπαφές χρήστη, και γ) γενικά εργαλεία ελέγχου και βελτιστοποίησης. Το πιθανό περιθώριο εξοικονόμησης ενέργειας χρησιμοποιώντας εξελιγμένες στρατηγικές ελέγχου παρουσιάζεται με τη βοήθεια πειραμάτων προσομοίωσης: υπάρχουν σημαντικά ενεργειακά οφέλη από τη χρήση εξελιγμένων στρατηγικών ελέγχου, συγκριτικά με παραδοσιακές μεθόδους ελέγχου βασισμένες σε κανόνες

Sense‐Think‐Act Framework for Intelligent Building Energy Management

The realization of smart and energetically efficient buildings is contingent upon the successful implementation of two tasks that occur on distinct phases of the building life cycle: in the design and subsequent retrofitting phases, the selection and implementation of an effective energy concept, and, during the operation phase, the actuation of energy systems to ensure parsimonious energy use while retaining acceptable end‐user thermal comfort. Operational efficiencies are achieved through the use of Building Energy Management Systems tasked to deliver core Sense, Think, Act (STA) functionalities: Sense, using sensing modalities installed in the building; Think, utilizing, typically a rule‐based decision system; and Act, by sending actuation commands to controllable building elements. Providing the intelligence in this STA process can be a formidable task due to the complex interplay of many systems and occurrence of disturbances. In this article, an architectural and algorithmic framework is presented to provide streamlined implementation of this process. Important ingredients in this framework are: (S) a data access component capable of collecting and aggregating information from a number of heterogeneous sources (sensors, weather stations, weather forecasts); (T) a model‐based optimization methodology to generate intelligent operational decisions; and (A) an assessment and actuation component. An illustrative application of the proposed methodology in an office building is provided

A Sense-Think-Act Methodology for Intelligent Building Energy Management

The realization of smart and energetically-efficient buildings is contingent upon the successful implementation of two tasks occurring on disparate phases of the building lifecycle: in the design and subsequent retrofitting phases, the selection and implementation of an effective energy concept; and, during the operation phase, the actuation of available energy-influencing systems and devices to ensure parsimonious use of resources while retaining thermal comfort at acceptable levels. Building Energy Management Systems are tasked to continuously implement a three-step Sense, Think, Act (STA) process: Sense, using sensing modalities installed in the building; Think, utilizing, typically a rule-based decision system; and Act, by sending actuation commands to controllable building elements. Providing the intelligence in this STA process – justifying in that sense the epithet "smart" in smart buildings – can oftentimes be a formidable task due to the complex interplay of many parameters and uncertainties. In this paper, a methodology developed within the European FP7-ICT Project PEBBLE, is presented to streamline the effective implementation of such STA processes. The ingredients of the proposed architecture are: (S) a middleware component capable of collecting and aggregating information from a number of inhomogeneous sources (sensors, weather stations, weather forecasts); (T) a model-based optimization methodology to automatically generate intelligent decisions; and (A) the Actuation layer, which communicates the decisions to the building. Information provided through graphical user interfaces, aims at enhancing userenergy-awareness and at making building users proactive Actors in the process. The ICT components implementing the methodology are presented and evaluated with corroborating experiments conducted in an office building of the Technical University of Crete

Sense‐Think‐Act Framework for Intelligent Building Energy Management

The realization of smart and energetically efficient buildings is contingent upon the successful implementation of two tasks that occur on distinct phases of the building life cycle: in the design and subsequent retrofitting phases, the selection and implementation of an effective energy concept, and, during the operation phase, the actuation of energy systems to ensure parsimonious energy use while retaining acceptable end‐user thermal comfort. Operational efficiencies are achieved through the use of Building Energy Management Systems tasked to deliver core Sense, Think, Act (STA) functionalities: Sense, using sensing modalities installed in the building; Think, utilizing, typically a rule‐based decision system; and Act, by sending actuation commands to controllable building elements. Providing the intelligence in this STA process can be a formidable task due to the complex interplay of many systems and occurrence of disturbances. In this article, an architectural and algorithmic framework is presented to provide streamlined implementation of this process. Important ingredients in this framework are: (S) a data access component capable of collecting and aggregating information from a number of heterogeneous sources (sensors, weather stations, weather forecasts); (T) a model‐based optimization methodology to generate intelligent operational decisions; and (A) an assessment and actuation component. An illustrative application of the proposed methodology in an office building is provided

The NOPTILUS project: Autonomous multi-AUV navigation for exploration of unknown environments

Current multi-AUV systems are far from being capable of fully autonomously taking over real-life complex situation-awareness operations. As such operations require advanced reasoning and decision-making abilities, current designs have to heavily rely on human operators. The involvement of humans, however, is by no means a guarantee of performance; humans can easily be over-whelmed by the information overload, fatigue can act detrimentally to their performance, properly coordinating vehicles actions is hard, and continuous operation is all but impossible. Within the European funded project NOPTILUS we take the view that an effective fully-autonomous multi-AUV concept/system, is capable of overcoming these shortcomings, by replacing human-operated operations by a fully autonomous one. In this paper, we present a new approach that is able to efficiently and fully-autonomously navigate a team of AUVs when deployed in exploration of unknown static and dynamic environments towards providing accurate static/dynamic maps of the environment. Additionally to achieving to efficiently and fully-autonomously navigate the AUV team, the proposed approach possesses certain advantages such as its extremely computational simplicity and scalability, and the fact that it can very straightforwardly embed and type of physical or other constraints and limitations (e.g., obstacle avoidance, nonlinear sensor noise models, localization fading environments, etc)

Simulation Assisted Implementation of a Model Based Control Parameter Fine-tuning Methodology for a Non-residential Building With a Complex Energy System

In order to improve the energy efficiency, while still insuring indoor comfort for a new non-residential building, a model-assisted fine-tuning methodology for control parameters was implemented. During offline experiments, where no control parameters are sent] to the building, the method was configured according to the particularities of the building. To this purpose we used a co-simulation, where the optimization algorithm for the control parameters was connected to a simulation model of the building and its technical equipment. The offline experiments show promising results regardmg energy savings when compared to good rule-based controllers. The transition to online experiments is more challenging as it depends on the way the whole building system behaves. (PDF) Simulation assisted implementation of a model based control parameter fine-tuning methodology for a nonresidential building with a complex energy system

Demand-shifting using model assisted control

Increasing energy demand and more strict environmental regulations have enabled the transition from traditional electric grids, in which centralized power plants directly transmit energy to consumers, to smart electrical grids where the existing power grid is enhanced by distributed, small-scale renewables-based energy generation systems. The use of renewables into the grid inserts uncertainty to the system, due to their stochastic output profile which strongly depends on local weather conditions and their inability to cover the total electric demand at peak demand periods. One of the most common and effective methods for tackling the peak demand period problem in buildings is the use of electricity tariffs with Time-Of-Use charges, which are based on higher energy rates during high demand periods, in order to encourage electricity load shifting from peak-demand periods to periods with lower demand. In the present work, we assume that a day-ahead notification on a (hypothetical) tariff plan is provided, and the Building Energy Management System is requested to adapt to the new setting. A photovoltaic panel is assumed available, but there is no energy storage and selling-back energy to the grid is not allowed. This way, the controller is forced to operate the actuating components of the building during the production of renewable energy, while saving energy the remaining time. A model-assisted control design methodology, utilizing a stochastic optimization algorithm and weather and occupancy predictions, is used to produce a control strategy which exploits the energy produced by the photovoltaic panel, to minimize the cooling cost of a building, for a given summer day, while maintaining user comfort at acceptable levels. The proposed approach results to substantial cost reductions when compared - for a hypothetical tariff scheme -to a widely-used, static rule-based control scheme

A Common Optimization Framework for Multi-Robot Exploration and Coverage in 3D Environments

International audienceThis paper studies the problems of static coverage and autonomous exploration of unknown three-dimensional environments with a team of cooperating aerial vehicles. Although these tasks are usually considered separately in the literature, we propose a common framework where both problems are formulated as the maximization of online acquired information via the definition of single-robot optimization functions, which differs only slightly in the two cases to take into account the static and dynamic nature of coverage and exploration respectively. A common derivative-free approach based on a stochastic approximation of these functions and their successive optimization is proposed, resulting in a fast and decentralized solution. The locality of this methodology limits however this solution to have local optimality guarantees and specific additional layers are proposed for the two problems to improve the final performance. Specifically, a Voronoi-based initialization step is added for the coverage problem and a combination with a frontier-based approach is proposed for the exploration case. The resulting algorithms are finally tested in simulations and compared with possible alternatives