Efficient state-space inference of periodic latent force models

Latent force models (LFM) are principled approaches to incorporating solutions to differen-tial equations within non-parametric inference methods. Unfortunately, the developmentand application of LFMs can be inhibited by their computational cost, especially whenclosed-form solutions for the LFM are unavailable, as is the case in many real world prob-lems where these latent forces exhibit periodic behaviour. Given this, we develop a newsparse representation of LFMs which considerably improves their computational efficiency,as well as broadening their applicability, in a principled way, to domains with periodic ornear periodic latent forces. Our approach uses a linear basis model to approximate onegenerative model for each periodic force. We assume that the latent forces are generatedfrom Gaussian process priors and develop a linear basis model which fully expresses thesepriors. We apply our approach to model the thermal dynamics of domestic buildings andshow that it is effective at predicting day-ahead temperatures within the homes. We alsoapply our approach within queueing theory in which quasi-periodic arrival rates are mod-elled as latent forces. In both cases, we demonstrate that our approach can be implemented efficiently using state-space methods which encode the linear dynamic systems via LFMs.Further, we show that state estimates obtained using periodic latent force models can re-duce the root mean squared error to 17% of that from non-periodic models and 27% of thenearest rival approach which is the resonator model (S ̈arkk ̈a et al., 2012; Hartikainen et al.,2012.

Efficient State-Space Inference of Periodic Latent Force Models

Latent force models (LFM) are principled approaches to incorporating solutions to differential equations within non-parametric inference methods. Unfortunately, the development and application of LFMs can be inhibited by their computational cost, especially when closed-form solutions for the LFM are unavailable, as is the case in many real world problems where these latent forces exhibit periodic behaviour. Given this, we develop a new sparse representation of LFMs which considerably improves their computational efficiency, as well as broadening their applicability, in a principled way, to domains with periodic or near periodic latent forces. Our approach uses a linear basis model to approximate one generative model for each periodic force. We assume that the latent forces are generated from Gaussian process priors and develop a linear basis model which fully expresses these priors. We apply our approach to model the thermal dynamics of domestic buildings and show that it is effective at predicting day-ahead temperatures within the homes. We also apply our approach within queueing theory in which quasi-periodic arrival rates are modelled as latent forces. In both cases, we demonstrate that our approach can be implemented efficiently using state-space methods which encode the linear dynamic systems via LFMs. Further, we show that state estimates obtained using periodic latent force models can reduce the root mean squared error to 17% of that from non-periodic models and 27% of the nearest rival approach which is the resonator model.Comment: 61 pages, 13 figures, accepted for publication in JMLR. Updates from earlier version occur throughout article in response to JMLR review

Forecasting Indoor Environment using Ensemble-based Data Assimilation Algorithms

Forecasting simulations of building environment have attracted growing interests since more and more applications have been explored. Occupant’s thermal comfort, safety and energy efficiency are reported to directly benefit from accurate predicted building physical conditions. Among all available research regarding forecasting indoor environment, there are substantially fewer studies relating to occupant safety and emergency forecasting and response than that of comfort and energy savings. This may due to the nature that the forecasting simulations associated with life safety concerns demand higher accuracy. Although the tasks of forecasting potential threats in the indoor environment are especially challenging, the benefits can be significant. For example, toxic contaminants such as carbon monoxide from fire smoke can be monitored and removed before the concentration reaches a harmful level. The sudden release of hazardous gases or the smoke generated from an accidental fire can also be detected and analyzed. Then, based on the results of forecasting simulations, the building control system can provide an efficient evacuation plan for all occupants in the building. However, by using traditional simulation tools that utilize one set of initial inputs to forecast future physical states, the predicted physical conditions may depart from reality as the simulation progresses over time. In this thesis, forecasting simulations of building safety management are improved by applying the theory of data assimilation where the simulation results are aided by the sensor measurements. Instead of studying methods that require high computational resources, this research focuses on affordable approaches, ensemble-based algorithms, to forecast indoor environment to solve various safety problems including forecasting indoor contaminant and smoke transport. The resulting models are able to provide predictions with noticeable accuracy by only using affordable computer resources such as a regular PC. Finally, a scaled compartment fire experiment is conducted to verify the real-time predictability of the model. The results indicate that the proposed method is able to forecast real-time fire smoke transport with significant lead time. Overall, the method of Ensemble Kalman Filter (EnKF) is efficient to apply to forecasting indoor contaminant and smoke transport problems. In the end of this thesis, suggestions are summarized to help those who would like to apply EnKF to solve other building simulation problems

Modelling of an axial flow compact separator using neural network

A novel design axial flow cyclonic separator called I-SEP was tested with an extensive set of experiments using air-water two phase flow mixture at atmospheric pressure. These experiments provided valuable data on the separation efficiency and pressure drop under different inlet conditions. The performance parameters i.e. Gas Carry Under (GCU) and Liquid Carry Over (LCO) were found to be non-linearly related to the inlet operating conditions. However it was found that resistance on the tangential outlet of the I-SEP affects the GCU and that manipulating the pressure difference between the two outlets and the inlet of the I-SEP through manual control valves, the GCU could be controlled. The separator was also extensively tested and compared with a gravity separator, when they were placed at the exit of a riser, in severe slugging condition frequently encountered in the production pipe work from some oil fields. The tests revealed that the I-SEP has better tendency to suppress severe slugging as compared to the gravity separator. A framework for neural network based on multiple types of input was also developed to model the separation performance of the I-SEP. Mutual Information (one of the key elements of the information theory) was applied to select the appropriate candidate input variables to the neural network framework. This framework was then used to develop a neural network model based on dimensionless input parameters such as pressure coefficient. This neural network model produced satisfactory prediction on unseen experimental data. The inverse function of a trained neural network was combined with a PID controller in a closed loop to control the GCU and LCO at a given set point by predicting the manipulating variable i.e. pressure at the I-SEP outlets. This control scheme was simulated using the test data. Such controller could be used to assist the operator in maintaining and controlling the GCU or LCO at the I-SEP outlets.The work performed during this study also includes the development of a data repository system to store and query the experimental result. An internet based framework is also developed that allows remote access of the experimental data using internet or wireless mobile devices

Advances in Modeling of Fluid Dynamics

This book contains twelve chapters detailing significant advances and applications in fluid dynamics modeling with focus on biomedical, bioengineering, chemical, civil and environmental engineering, aeronautics, astronautics, and automotive. We hope this book can be a useful resource to scientists and engineers who are interested in fundamentals and applications of fluid dynamics

New mathematical approaches to the quantification of uncertainty affecting the measurement of U-value

This thesis describes the development and validation of a new computational procedure for the calculation of thermal transmittance (U-value) of existing building elements from the measurement of surface heat flux, and surface and nearby air temperatures. The U-value plays a key role in the determination of the final energy consumption of a dwelling, and, as in the current political scenario reducing carbon emissions is a growing concern, obtaining accurate and quick measurements of thermal transmittance is of particular relevance to the precise representation of the energy performance of the building sector. The calculation method developed is an extension of the RC network, a model based on the discretisation of building elements in resistors and capacitors in analogy with electrical circuits. The advances proposed in this work extend the discrete RC networks to a model based on the full heat equation, with continuous, spatially varying thermal prop- erties. The solution algorithm is inserted in a Bayesian framework that allows the reformulation of the problem in terms of probability distributions. Two solution schemes have been confronted: Markov Chain Monte Carlo and Ensemble Kalman Filters approximation. The model proposed has been validated on synthetic data, laboratory data collected in an environmental chamber on a solid and cavity wall, and in-situ data collected in 3 different locations (2 solid walls and 1 insulated steel frame construction). The results show that the model offers an improved characterisation of the heat transfer through the building elements, furthermore, the algorithm can be used to analyse different wall constructions without the necessity of changing the structure of the model, as opposed to the standard RC networks, and, finally, it offers the practical advantages of the uncertainty reduction on thermal transmittance (from 14-25% to 7-10%) and a diminution of the necessary monitoring period from a minimum of 3 days to 1 day or less. These advantages, in turn, benefit the building performance evaluation on different levels: in first instance, the practicality of measuring thermal transmittance in-situ is improved, thus making it easier to monitor the actual envelope performance and, secondly, the uncertainty reduction on the U-value leads to important reductions on the uncertainty surrounding the energy consumption predictions associated with a dwelling

Net-zero Building Cluster Simulations and On-line Energy Forecasting for Adaptive and Real-Time Control and Decisions

Buildings consume about 41.1% of primary energy and 74% of the electricity in the U.S. Moreover, it is estimated by the National Energy Technology Laboratory that more than 1/4 of the 713 GW of U.S. electricity demand in 2010 could be dispatchable if only buildings could respond to that dispatch through advanced building energy control and operation strategies and smart grid infrastructure. In this study, it is envisioned that neighboring buildings will have the tendency to form a cluster, an open cyber-physical system to exploit the economic opportunities provided by a smart grid, distributed power generation, and storage devices. Through optimized demand management, these building clusters will then reduce overall primary energy consumption and peak time electricity consumption, and be more resilient to power disruptions. Therefore, this project seeks to develop a Net-zero building cluster simulation testbed and high fidelity energy forecasting models for adaptive and real-time control and decision making strategy development that can be used in a Net-zero building cluster. The following research activities are summarized in this thesis: 1) Development of a building cluster emulator for building cluster control and operation strategy assessment. 2) Development of a novel building energy forecasting methodology using active system identification and data fusion techniques. In this methodology, a systematic approach for building energy system characteristic evaluation, system excitation and model adaptation is included. The developed methodology is compared with other literature-reported building energy forecasting methods; 3) Development of the high fidelity on-line building cluster energy forecasting models, which includes energy forecasting models for buildings, PV panels, batteries and ice tank thermal storage systems 4) Small scale real building validation study to verify the performance of the developed building energy forecasting methodology. The outcomes of this thesis can be used for building cluster energy forecasting model development and model based control and operation optimization. The thesis concludes with a summary of the key outcomes of this research, as well as a list of recommendations for future work.Ph.D., Civil Engineering -- Drexel University, 201

Power Electronics and Energy Management for Battery Storage Systems

The deployment of distributed renewable generation and e-mobility systems is creating a demand for improved dynamic performance, flexibility, and resilience in electrical grids. Various energy storages, such as stationary and electric vehicle batteries, together with power electronic interfaces, will play a key role in addressing these requests thanks to their enhanced functionality, fast response times, and configuration flexibility. For the large-scale implementation of this technology, the associated enabling developments are becoming of paramount importance. These include energy management algorithms; optimal sizing and coordinated control strategies of different storage technologies, including e-mobility storage; power electronic converters for interfacing renewables and battery systems, which allow for advanced interactions with the grid; and increase in round-trip efficiencies by means of advanced materials, components, and algorithms. This Special Issue contains the developments that have been published b researchers in the areas of power electronics, energy management and battery storage. A range of potential solutions to the existing barriers is presented, aiming to make the most out of these emerging technologies

New mathematical approaches to the quantification of uncertainty affecting the measurement of U-value

This thesis describes the development and validation of a new computational procedure for the calculation of thermal transmittance (U-value) of existing building elements from the measurement of surface heat flux, and surface and nearby air temperatures. The U-value plays a key role in the determination of the final energy consumption of a dwelling, and, as in the current political scenario reducing carbon emissions is a growing concern, obtaining accurate and quick measurements of thermal transmittance is of particular relevance to the precise representation of the energy performance of the building sector. The calculation method developed is an extension of the RC network, a model based on the discretisation of building elements in resistors and capacitors in analogy with electrical circuits. The advances proposed in this work extend the discrete RC networks to a model based on the full heat equation, with continuous, spatially varying thermal prop- erties. The solution algorithm is inserted in a Bayesian framework that allows the reformulation of the problem in terms of probability distributions. Two solution schemes have been confronted: Markov Chain Monte Carlo and Ensemble Kalman Filters approximation. The model proposed has been validated on synthetic data, laboratory data collected in an environmental chamber on a solid and cavity wall, and in-situ data collected in 3 different locations (2 solid walls and 1 insulated steel frame construction). The results show that the model offers an improved characterisation of the heat transfer through the building elements, furthermore, the algorithm can be used to analyse different wall constructions without the necessity of changing the structure of the model, as opposed to the standard RC networks, and, finally, it offers the practical advantages of the uncertainty reduction on thermal transmittance (from 14-25% to 7-10%) and a diminution of the necessary monitoring period from a minimum of 3 days to 1 day or less. These advantages, in turn, benefit the building performance evaluation on different levels: in first instance, the practicality of measuring thermal transmittance in-situ is improved, thus making it easier to monitor the actual envelope performance and, secondly, the uncertainty reduction on the U-value leads to important reductions on the uncertainty surrounding the energy consumption predictions associated with a dwelling