Control of Residential Space Heating for Demand Response Using Grey-box Models

Certain advanced control schemes are capable of making a part of the thermostatic loads of space heating in buildings flexible, thereby enabling buildings to engage in so-called demand response. It has been suggested that this flexible consumption may be a valuable asset in future energy systems where conventional fossil fuel-based energy production have been partially replaced by intermittent energy production from renewable energy sources. Model predictive control (MPC) is a control scheme that relies on a model of the building to predict the future impact on the temperature conditions in the building of both control decisions (space heating) and phenomena outside the influence of the control scheme (e.g. weather conditions). MPC has become one of the most frequently used control schemes in studies investigating the potential for engaging buildings in demand response. While research has indicated MPC to have many useful applications in buildings, several challenges still inhibit its adoption in practice. A significant challenge related to MPC implementation lies in obtaining the required model of the building, which is often derived from measurements of the temperature and heating consumption. Furthermore, studies have indicated that, although demand response in buildings could contribute to the task of balancing supply and demand, suitable tariff structures that incentivize consumers to engage in DR are lacking. The main goal of this work is to contribute with research that addresses these issues. This thesis is divided into two parts.The first part of the thesis explores ways of simplifying the task of obtaining the building model that is required for implementation of MPC. Studies that explore practical ways of obtaining the measurement data needed for model identification are presented together with a study evaluating the suitedness of different low-order model structures that are suited for control-purposes.The second part of the thesis presents research on the potential of utilizing buildings for demand response. First, two studies explore and evaluate suitable incentive mechanisms for demand response by implementing an MPC scheme in a multi-apartment building block. These studies evaluate two proposed incentive mechanisms as well as the impact of building characteristics and MPC scheme implementation. Finally, a methodology for bottom-up modelling of entire urban areas is presented, and proved capable of predicting the aggregated energy demand of urban areas. The models resulting from the methodology are then applied in an analysis on demand response

Data Science: Measuring Uncertainties

With the increase in data processing and storage capacity, a large amount of data is available. Data without analysis does not have much value. Thus, the demand for data analysis is increasing daily, and the consequence is the appearance of a large number of jobs and published articles. Data science has emerged as a multidisciplinary field to support data-driven activities, integrating and developing ideas, methods, and processes to extract information from data. This includes methods built from different knowledge areas: Statistics, Computer Science, Mathematics, Physics, Information Science, and Engineering. This mixture of areas has given rise to what we call Data Science. New solutions to the new problems are reproducing rapidly to generate large volumes of data. Current and future challenges require greater care in creating new solutions that satisfy the rationality for each type of problem. Labels such as Big Data, Data Science, Machine Learning, Statistical Learning, and Artificial Intelligence are demanding more sophistication in the foundations and how they are being applied. This point highlights the importance of building the foundations of Data Science. This book is dedicated to solutions and discussions of measuring uncertainties in data analysis problems

BAYESIAN MODELLING OF ULTRA HIGH-FREQUENCY FINANCIAL DATA

The availability of ultra high-frequency (UHF) data on transactions has revolutionised data processing and statistical modelling techniques in finance. The unique characteristics of such data, e.g. discrete structure of price change, unequally spaced time intervals and multiple transactions have introduced new theoretical and computational challenges. In this study, we develop a Bayesian framework for modelling integer-valued variables to capture the fundamental properties of price change. We propose the application of the zero inflated Poisson difference (ZPD) distribution for modelling UHF data and assess the effect of covariates on the behaviour of price change. For this purpose, we present two modelling schemes; the first one is based on the analysis of the data after the market closes for the day and is referred to as off-line data processing. In this case, the Bayesian interpretation and analysis are undertaken using Markov chain Monte Carlo methods. The second modelling scheme introduces the dynamic ZPD model which is implemented through Sequential Monte Carlo methods (also known as particle filters). This procedure enables us to update our inference from data as new transactions take place and is known as online data processing. We apply our models to a set of FTSE100 index changes. Based on the probability integral transform, modified for the case of integer-valued random variables, we show that our models are capable of explaining well the observed distribution of price change. We then apply the deviance information criterion and introduce its sequential version for the purpose of model comparison for off-line and online modelling, respectively. Moreover, in order to add more flexibility to the tails of the ZPD distribution, we introduce the zero inflated generalised Poisson difference distribution and outline its possible application for modelling UHF data

Activity recognition using Grey-Markov model

Activity Recognition (AR) is a process of identifying actions and goals of one or more agents of interest. AR techniques have been applied to both large and small scale activity identification. Examples of AR techniques include Genetic Algorithm, Markov Chain, and so on. This research proposes a novel method, Grey Markov Model (GMM), for detection and prediction of pre-defined activities. There were three objectives of this research. The first objective was to establish a database of pre-defined human activities. The second objective was to establish the Grey Markov Model. The final objective was to verify the model performance using the established database. This thesis describes the methodology of test setup and data collection, as well as the procedures of model generation. Furthermore, experimental results of model performance verification test are also reported

Critical Market Crashes

This review is a partial synthesis of the book ``Why stock market crash'' (Princeton University Press, January 2003), which presents a general theory of financial crashes and of stock market instabilities that his co-workers and the author have developed over the past seven years. The study of the frequency distribution of drawdowns, or runs of successive losses shows that large financial crashes are ``outliers'': they form a class of their own as can be seen from their statistical signatures. If large financial crashes are ``outliers'', they are special and thus require a special explanation, a specific model, a theory of their own. In addition, their special properties may perhaps be used for their prediction. The main mechanisms leading to positive feedbacks, i.e., self-reinforcement, such as imitative behavior and herding between investors are reviewed with many references provided to the relevant literature outside the confine of Physics. Positive feedbacks provide the fuel for the development of speculative bubbles, preparing the instability for a major crash. We demonstrate several detailed mathematical models of speculative bubbles and crashes. The most important message is the discovery of robust and universal signatures of the approach to crashes. These precursory patterns have been documented for essentially all crashes on developed as well as emergent stock markets, on currency markets, on company stocks, and so on. The concept of an ``anti-bubble'' is also summarized, with two forward predictions on the Japanese stock market starting in 1999 and on the USA stock market still running. We conclude by presenting our view of the organization of financial markets.Comment: Latex 89 pages and 38 figures, in press in Physics Report

The impact of macroeconomic leading indicators on inventory management

Forecasting tactical sales is important for long term decisions such as procurement and informing lower level inventory management decisions. Macroeconomic indicators have been shown to improve the forecast accuracy at tactical level, as these indicators can provide early warnings of changing markets while at the same time tactical sales are sufficiently aggregated to facilitate the identification of useful leading indicators. Past research has shown that we can achieve significant gains by incorporating such information. However, at lower levels, that inventory decisions are taken, this is often not feasible due to the level of noise in the data. To take advantage of macroeconomic leading indicators at this level we need to translate the tactical forecasts into operational level ones. In this research we investigate how to best assimilate top level forecasts that incorporate such exogenous information with bottom level (at Stock Keeping Unit level) extrapolative forecasts. The aim is to demonstrate whether incorporating these variables has a positive impact on bottom level planning and eventually inventory levels. We construct appropriate hierarchies of sales and use that structure to reconcile the forecasts, and in turn the different available information, across levels. We are interested both at the point forecast and the prediction intervals, as the latter inform safety stock decisions. Therefore the contribution of this research is twofold. We investigate the usefulness of macroeconomic leading indicators for SKU level forecasts and alternative ways to estimate the variance of hierarchically reconciled forecasts. We provide evidence using a real case study