Ilche Georgievski

Optimizing Energy Costs for Offices Connected to the Smart Grid

In addition to providing for a more reliable distribution infrastructure, the smart grid promises to give the end users better pricing and usage information. It is thus interesting for them to be ready to take advantage of features such as dynamic energy pricing and real-time choice of operators. In this work, we propose a system to monitor and control an office environment and to couple it with the smart grid. The idea is to schedule the operation of devices according to policies defined by the users, in order to minimize the cost of operation while leaving unaffected user comfort and productivity. The implementation of the system and its testing in a living lab environment show interesting economic savings of an average of about 35% and in some cases even overall energy savings in the order of 10% for a building equipped with renewable generation plants, and economic and energy savings of 20% and 10%, respectively, for a building without local renewable installations.

HTN planning

Hierarchies are one of the most common structures used to understand and conceptualise the world. Within the field of Artificial Intelligence (AI) planning, which deals with the automation of world-relevant problems, Hierarchical Task Network (HTN) planning is the branch that represents and handles hierarchies. In particular, the requirement for rich domain knowledge to characterise the world enables HTN planning to be very useful, and also to perform well. However, the history of almost 40 years obfuscates the current understanding of HTN planning in terms of accomplishments, planning models, similarities and differences among hierarchical planners, and its current and objective image. On top of these issues, the ability of hierarchical planning to truly cope with the requirements of real-world applications has been often questioned. As a remedy, we propose a framework-based approach where we first provide a basis for defining different formal models of hierarchical planning, and define two models that comprise a large portion of HTN planners. Second, we provide a set of concepts that helps in interpreting HTN planners from the aspect of their search space. Then, we analyse and compare the planners based on a variety of properties organised in five segments, namely domain authoring, expressiveness, competence, computation and applicability. Furthermore, we select Web service composition as a real-world and current application, and classify and compare the approaches that employ HTN planning to solve the problem of service composition. Finally, we conclude with our findings and present directions for future work. In summary, we provide a novel and comprehensive viewpoint on a core AI planning technique.

Combining Activity Recognition and AI Planning for Energy-Saving Offices

Energy-saving offices require autonomous and optimised control of integrated devices and appliances with the objective of saving energy while the occupant comfort and productivity are preserved. We propose an approach that analyses and controls an office space and accounts for the objectives of energy-saving offices. The approach considers ontology-based occupant activity recognition using simple sensors to process the context information, and employs Artificial Intelligence planning to control appliances. The approach is evaluated in a semi-simulated setting. The activity recognition strategy is tested in an actual living lab and shows recognising accuracy of about 80%. The planning technique is able to cope efficiently under a simulated and increasing number of offices and recognised activities. The overall solution shows intriguing potential for energy saving in the order of 70%, given mostly sunny days and a provisional set of devices for experimentation.

Automated Planning for Ubiquitous Computing

The goal of ubiquitous computing is to create ambience in which one’s experiences and quality of life are improved by monitoring and assisting people using ubiquitous technologies and computation in coherence. The continuous advancements of involved technologies, such as wireless communications, mobile devices, and sensors, imply fast evolution of ubiquitous computing environments too. The complexity of these environments is reaching a point where traditional solutions simply no longer work. The environments are in need of computational techniques that can deal with the evolution and uncertainty of ubiquitous computing environments dynamically and automatically. Artificial Intelligence (AI) can contribute towards satisfying this future scenario in many ways, while numerous approaches inspired by work in the AI planning community have already been designed for ubiquitous computing. We devote this study to investigate the current progress of AI planning for ubiquitous computing by analysing those approaches. We rigorously search for and select relevant literature out of which we extract qualitative information. Using the extracted qualities, we derive a generic framework that consists of aspects important to planning for ubiquitous computing. The framework’s main purpose is to facilitate the understanding of those aspects, and classify the literature according to them. We then analyse the literature in a consolidated way, and identify future challenges of planning for ubiquitous computing.

Utility-based HTN planning

We propose the use of HTN planning for risk-sensitive planning domains. We suggest utility functions that reflect the risk attitude of compound tasks, and adapt a best-first search algorithm to take such utilities into account.

Planning meets activity recognition: Service coordination for intelligent buildings

Abstract Building managers need effective tools to improve occupants’ experiences considering constraints of energy efficiency. Current building management systems are limited to coordinating device services in simple and prefixed situations. Think of an office with lights offering services, such as turn on a light, which are invoked by the system to automatically control the lights. In spite of the evident potential for energy saving, the office occupants often end up in the dark, they have too much light when working with computers, or unnecessary lights are turned on. The office is thus not aware of the occupants’ presence nor anticipates their activities. Our proposal is to coordinate services while anticipating occupant activities with sufficient accuracy. Finding and composing services that will support occupant activities is however a complex problem. The high number of services, the continuous transformation of buildings, and the various building standards imply a search through a vast number of possible contextual situations every time occupants perform activities. Our solution to this building coordination problem is based on Hierarchical Task Network (HTN) planning in combination with activity recognition. While HTN planning provides powerful means for composing services automatically, activity recognition is needed to identify occupant activities as soon as they occur. The output of this combination is a sequence of services that needs to be executed under the uncertainty of building environments. Our solution supports continuous context changes and service failures by using an advanced orchestration strategy. We design, implement and deploy a system in two cases, namely offices and a restaurant, in our own office building at the University of Groningen. We show energy savings in the order of 80% when compared to manual control in both cases, and 60% when compared to using only movement sensors. Moreover, we show that one can save a figure of €600 annually for the electricity costs of the restaurant. We use a survey to evaluate the experience of restaurant occupants. The majority of them are satisfied with the solution and find it useful. Finally, the technical evaluation provides insights into the efficiency of our system.

Service-Oriented and Cloud Computing

We present a compositional construction of Web Services, using Reo and Constraint Automata as the main “glue” ingredients. Reo is a graphical and exogenous coordination language based on channels. We propose a framework that, taking as input the behavioral description of services (as Constraint Automata), their WSDL interfaces, and the description of their interaction in Reo, generates all the necessary Java code to orchestrate the services in practice. For each Web Service, we automatically generate a proxy that manages the communication between this service and the Reo circuit. Although we focus on Web Services, we can compose different kinds of service-oriented and component technologies at the same time (e.g., CORBA, RPC, WCF), by generating different proxies and connecting them to the same coordinator.

Mining Sequential Patterns for Appliance Usage Prediction

Reducing the energy consumption in buildings and homes can be achieved by predicting how energy-consuming appliances are used, and by discovering their patterns. To mine these patterns, a smart-metering architecture needs to be in place complemented by appropriate data analysis mechanisms. Once the usage patterns are obtained, they can be employed to optimize the way energy from renewable installations, home batteries, and even micro grids is managed. We present an approach and related experiments for mining sequential patterns in appliance usage. In particular, we mine patterns that allow us to perform device usage prediction, energy usage prediction, and device usage prediction with failed sensors. The focus of this work is on the sequential relationships between the state of distinct devices. We use data sets from three existing buildings, of which two are households and one is an office building. The data is used to train our modified Support-Pruned Markov Models which use a relative support threshold. Our experiments show the viability of the approach, as we achieve an overall accuracy of 87% in device usage predictions, and up to 99% accuracy for devices that have the strongest sequential relationships. For these devices, the energy usage predictions have an accuracy of around 90%. Predicting device usage with failed sensors is feasible, assuming there is a strong sequential relationship for the devices.

Cloud Ready Applications Composed via HTN Planning

Modern software applications are increasingly deployed and distributed on infrastructures in the Cloud, and then offered as a service. Before the deployment process happens, these applications are being manually - or with some predefined scripts - composed from various smaller interdependent components. With the increase in demand for, and complexity of applications, the composition process becomes an arduous task often associated with errors and a suboptimal use of computer resources. To alleviate such a process, we introduce an approach that uses planning to automatically and dynamically compose applications ready for Cloud deployment. The industry may benefit from using automated planning in terms of support for product variability, sophisticated search in large spaces, fault tolerance, near-optimal deployment plans, etc. Our approach is based on Hierarchical Task Network (HTN) planning as it supports rich domain knowledge, component modularity, hierarchical representation of causality, and speed of computation. We describe a deployment using a formal component model for the Cloud, and we propose a way to define and solve an HTN planning problem from the deployment one. We employ an existing HTN planner to experimentally evaluate the feasibility of our approach.