Improving building energy efficiency through novel hybrid models and control approaches including a data center case study

The building sector consumes the most energy and emits the greatest quantity of greenhouse gases of any sector. Energy savings in this sector can make a major contribution to tackling the threat of climate change. Research has produced a variety of solutions, for example, net zero and positive-energy buildings. At the same time, both models and controls are being challenged by increasingly complex buildings equipped with advanced information and communications technologies (ICT). This dissertation addresses these challenges by proposing a multidisciplinary, wide-ranging modeling methodology that enables new strategies for saving building energy. The core methodology utilizes novel modeling approaches to improve predictive models and produce innovative energy solutions. Models are validated and investigated using a variety of buildings and controls. Data centers and demand controlled ventilation (DCV) are the focus because they represent both "multifunctional buildings" and general energy system controls. This dissertation makes the following seven original contributions: (1) The first systematic, complete case study of a data center in which infrastructure, energy and air management performance, and waste heat recovery systems were investigated, analyzed, and quantified using long-term power consumption data. (2) A novel and tuning-free DCV building control strategy that is far superior to proportional control and more competitive than proportional-integral-derivative (PID) control. (3) An artificial neural network (ANN) model for predicting the water evaporation rate in a swimming hall. (4) A new ANN model for estimating prediction intervals and accounts for the uncertainty of point estimation for indoor conditions in an office building. (5) A new Maximum Likelihood Estimation (MLE) model for predicting constant and time-varying air change rates and a coupled model for estimating the number of occupants in an office. (6) Discovery of a new physical law for run-around heat recovery systems that can be used to develop a simulation model to estimate the system performance for constant volume air (CAV) and DCV systems. This new law was verified in different sites. (7) A new hybrid numerical-ANN model for building performance simulation. The hybrid model can improve not only the model accuracy but also the generalizability of ANN. The results demonstrate the applicability of the modeling techniques and the models, and significant energy savings in buildings. The resulting improvements in model accuracy, forecasting capability, and energy efficiency were published in eight journals. By unifying the results of eight publications, this dissertation presents a comprehensive and coherent study that advances the state-of-the-art building energy research

Evolutionary Approach to Efficient Provisioning and Self-organization in Wireless Sensor Networks (WSN)

Advances in low-power digital integration and microelectro-mechanical systems (MEMS) have paved the way for micro-sensors. These sensors are equipped with data processing capabilities along with sensory circuits. Sensor data are processed on these individual sensors and transmitted to the target (sink). Lowcost integration and small sizes of these sensors have generated special interest in the area of disposable-sensors and large scale platform management. Queries to these sensors are addressed to nodes which have data satisfying the same condition. However, these sensors may be constrained in energy, bandwidth, storage, and processing capabilities. Large number of such sensors along with these constraints creates a sensor-management problem. At the network layer it amounts to setting up the efficient route that transmits the non-redundant data from source to the sink in order to maximize one or more sensor objectives (e.g. battery (and sensor's) life, Sensor-Data yield). This is done while adapting to changing connectivity due to failure of some nodes and new nodes powering up. First part of the thesis propose a reduced-complexity genetic algorithm (GA) for optimization of multi-hop battery-constrained sensor networks. The goal of the system is to generate optimal number of sensor-clusters with cluster-heads. It results in minimization of the power consumption of the sensor system while maximizing the sensor objectives (coverage and exposure). The genetic algorithm is used to adaptively create various components such as cluster-members, cluster-heads, and next-cluster. These components are then used to evaluate the average fitness of the system based on the sequence of communication links towards the sink. We then enhance the genetic algorithm (GA) approach for secure deployment of resource constrained multi-hop sensor networks. The goal in this case is to achieve secure coverage and improve battery life by dynamically optimizing security attributes (Like authentication and encryption). Further, we augment the GA approach for intrusion detection of resource constrained multi-hop sensor networks. Traditional intrusion detection mechanisms have limited applicability to the sensor networks due to scarce battery and processing resources. Therefore, we propose an effective scheme that would offer a power efficient and lightweight approach to identify malicious attacks. We evaluate sensor node attributes by measuring the perceived threat and its suitability to host local monitoring node (LMN) that acts as trusted proxy agent for the sink and capable of securely monitoring its neighbors. Security attributes in conjunction with genetic algorithm jointly optimizes the selection of monitoring nodes (i.e., LMN) by dynamically evaluating node fitness by profiling workloads patterns, packet statistics, utilization data, battery status, and quality-of-service compliance. Second part of the thesis delves into application of Information Technology (and Industrial) Systems and devices where the use of sensor networks can deliver non-intrusive and effective telemetry for group-based server management. These systems (Like Data Centers or Shipment tracking) face major challenges in seamless integration of telemetry and control data that is essential to various autonomic management functions related to power, thermal, reliability, predictability, survivability, locality and adaptability. Such systems that are supported by a dense network of sense-points operating in noisy environment (Metals, Cables) are required to deliver reliable trends, measurements and analysis in a timely fashion. The traditional approaches to provide distributed observability and control using wired solutions are static, expensive, and nonscalable. We apply the proposed GA approach for this unique environment that replaces static wired sensors with dynamically reconfigurable battery-powered wireless sensors. The proposed technique employs machine learning approach to optimize sensor node function assignment, clustering decisions, route establishment and data collection trees for improved throughput that results in effective controls

Efficient Learning Machines

Computer scienc

Self-tuning PID-type Fuzzy Adaptive Control for CRAC in Datacenters

International audienceIn order to eliminate the current negative condition of Automatic Computer Room Air-Conditioning (CRAC) system, self-tuning Fuzzy Logic Control (FLC) was designed and applied to fan speed in CRAC system. In this paper, we derive a thermodynamic model of a datacenter suitable for applying adaptive self-tuning PID-type fuzzy adaptive control theory. It combines the classic PID control strategy and fuzzy adaptive control theory. The classic PID control uses the error and rate of change of error as its inputs to control the temperature automatically, and the fuzzy logic controller is used in the self-tuning PID-type fuzzy control to tune the parameters of PID controller on-line by fuzzy control rules. Simulation and testing results show that the proposed self-tuning FLC method can achieve less steady-state error and short settling time in temperature control of datacenter

Animating the Ethical Demand:Exploring user dispositions in industry innovation cases through animation-based sketching

This paper addresses the challenge of attaining ethical user stances during the design process of products and services and proposes animation-based sketching as a design method, which supports elaborating and examining different ethical stances towards the user. The discussion is qualified by an empirical study of Responsible Research and Innovation (RRI) in a Triple Helix constellation. Using a three-week long innovation workshop, UCrAc, involving 16 Danish companies and organisations and 142 students as empirical data, we discuss how animation-based sketching can explore not yet existing user dispositions, as well as create an incentive for ethical conduct in development and innovation processes. The ethical fulcrum evolves around Løgstrup's Ethical Demand and his notion of spontaneous life manifestations. From this, three ethical stances are developed; apathy, sympathy and empathy. By exploring both apathetic and sympathetic views, the ethical reflections are more nuanced as a result of actually seeing the user experience simulated through different user dispositions. Exploring the three ethical stances by visualising real use cases with the technologies simulated as already being implemented makes the life manifestations of the users in context visible. We present and discuss how animation-based sketching can support the elaboration and examination of different ethical stances towards the user in the product and service development process. Finally we present a framework for creating narrative representations of emerging technology use cases, which invite to reflection upon the ethics of the user experience.</jats:p