An online grey-box model based on unscented kalman filter to predict temperature profiles in smart buildings

Nearly 40% of primary energy consumption is related to the usage of energy in Buildings. Energy-related data such as indoor air temperature and power consumption of heating/cooling systems can be now collected due to the widespread diffusion of Internet-of-Things devices. Such energy data can be used (i) to train data-driven models than learn the thermal properties of buildings and (ii) to predict indoor temperature evolution. In this paper, we present a Grey-box model to estimate thermal dynamics in buildings based on Unscented Kalman Filter and thermal network representation. The proposed methodology has been applied in two different buildings with two different thermal network discretizations to test its accuracy in indoor air temperature prediction. Due to a lack of a real-world data sampled by Internet of Things (IoT) devices, a realistic data-set has been generated using the software Energy+, by referring to real industrial building models. Results on synthetic and realistic data show the accuracy of the proposed methodology in predicting indoor temperature trends up to the next 24 h with a maximum error lower than 2 °C, considering one year of data with different weather conditions

Final Causality in the Thought of Thomas Aquinas

Throughout his corpus, Thomas Aquinas develops an account of final causality that is both philosophically nuanced and interesting. The aim of my dissertation is to provide a systematic reconstruction of this account of final causality, one that clarifies its motivation and appeal. The body of my dissertation consists of four chapters. In Chapter 1, I examine the metaphysical underpinnings of Aquinas’s account of final causality by focusing on how Aquinas understands the causality of the final cause. I argue that Aquinas holds that an end is a cause because it is the determinate effect toward which an agent’s action is directed. I proceed by first presenting the general framework of causality within which Aquinas understands final causality. I then consider how Aquinas justifies the reality of each of the four kinds of cause, placing special emphasis on the final cause. In Chapter 2, I consider final causality from the perspective of goodness and explore the reasons why Aquinas thinks that the end of an action is always good. For even if one was convinced that the end of an action is indeed a cause, one might still resist attributing any normative or evaluative properties to the end, much less a positively-valenced normative property like goodness. In this chapter, I show how, given Aquinas’s metaphysics of powers and his characterization of goodness as that which all desire, it follows that every action is for the sake of some good. In Chapter 3, I consider Aquinas’s account of the relation between final causality and cognition. In many passages throughout his corpus—most famously in the fifth of his Five Ways—Aquinas advances the claim that cognition plays an essential role in final causality. In this chapter, I explore Aquinas’s account of the relation between final causality and cognition by reconstructing his Fifth Way and investigating the metaphysical foundations on which it rests. While the first three chapters of my dissertation focus on Aquinas’s account of final causality from the perspective of the ends of individual agents, in Chapter 4 I broaden my focus to consider the way in which the account of final causality developed in these earlier chapters shapes Aquinas’s philosophical cosmology. I argue that, on Aquinas’s view, when an individual agent acts for an end, it is plays a role in a larger system, e.g. a polis, an ecosystem, or the universe itself

Parameter sensitivity analysis for different complexity land surface models using multicriteria methods

A multicriteria algorithm, the MultiObjective Generalized Sensitivity Analysis (MOGSA), was used to investigate the parameter sensitivity of five different land surface models with increasing levels of complexity in the physical representation of the vegetation (BUCKET, CHASM, BATS 1, Noah, and BATS 2) at five different sites representing crop land/ pasture, grassland, rain forest, cropland, and semidesert areas. The methodology allows for the inclusion of parameter interaction and does not require assumptions of independence between parameters, while at the same time allowing for the ranking of several single-criterion and a global multicriteria sensitivity indices. The analysis required on the order of 50 thousand model runs. The results confirm that parameters with similar "physical meaning" across different model structures behave in different ways depending on the model and the locations. It is also shown that after a certain level an increase in model structure complexity does not necessarily lead to better parameter identifiability, i.e., higher sensitivity, and that a certain level of overparameterization is observed. For the case of the BATS 1 and BATS 2 models, with essentially the same model structure but a more sophisticated vegetation model, paradoxically, the effect on parameter sensitivity is mainly reflected in the sensitivity of the soil-related parameter. Copyright 2006 by the American Geophysical Union

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

Development and application of a framework for model structure evaluation in environmental modelling

In a fast developing world with an ever rising population, the pressures on our natural environment are continuously increasing, causing problems such as floods, water- and air pollution, droughts,... Insight in the driving mechanisms causing these threats is essential in order to properly mitigate these problems. During the last decades, mathematical models became an essential part of scientific research to better understand and predict natural phenomena. Notwithstanding the diversity of currently existing models and modelling frameworks, the identification of the most appropriate model structure for a given problem remains a research challenge. The latter is the main focus of this dissertation, which aims to improve current practices of model structure comparison and evaluation. This is done by making individual model decisions more transparent and explicitly testable. A diagnostic framework, focusing on a flexible and open model structure definition and specifying the requirements for future model developments, is described. Methods for model structure evaluation are documented, implemented, extended and applied on both respirometric and hydrological models. For the specific case of lumped hydrological models, the unity between apparently different models is illustrated. A schematic representation of these model structures provides a more transparent communication tool, while meeting the requirements of the diagnostic approach

Hindered and compression solid settling functions – sensor data collection, practical model identification and validation

Secondary settling tanks (SSTs) are the most hydraulically sensitive unit operations in activated sludge water resource recovery facilities (WRRF). Mathematical models for predicting activated sludge solids settling velocity include parameters that show irreducible epistemic uncertainty. Therefore, reliable and periodic calibration of the settling velocity model is key for predicting activated sludge process capacity, thus averting possible failures under wet-weather flow- and filamentous bulking conditions. The two main knowledge gaps addressed here are: (1) Do constitutive functions for hindered and compression settling exist, for which all velocity parameters can be uniquely estimated? (2) What is the optimum sensor data requirement of developing reliable settling velocity functions? Innovative settling column sensor and full-scale data were used to identify and validate amended Vesilind function for hindered settling and a new exponential function for compression settling velocity using one-dimensional and computational fluid dynamics simulations. Results indicate practical model identifiability under well-settling and filamentous bulking conditions

Practical identifiability analysis of environmental models

Identifiability of a system model can be considered as the extent to which one can capture its parameter values from observational data and other prior knowledge of the system. Identifiability must be considered in context so that the objectives of the modelling must also be taken into account in its interpretation. A model may be identifiable for certain objective functions but not others; its identifiability may depend not just on the model structure but also on the level and type of noise, and may even not be identifiable when there is no noise on the observational data. Context also means that non-identifiability might not matter in some contexts, such as when representing pluralistic values among stakeholders, and may be very important in others, such as where it leads to intolerable uncertainties in model predictions. Uncertainty quantification of environmental systems is receiving increasing attention especially through the development of sophisticated methods, often statistically-based. This is partly driven by the desire of society and its decision makers to make more informed judgments as to how systems are better managed and associated resources efficiently allocated. Less attention seems to be given by modellers to understand the imperfections in their models and their implications. Practical methods of identifiability analysis can assist greatly here to assess if there is an identifiability problem so that one can proceed to decide if it matters, and if so how to go about modifying the model (transforming parameters, selecting specific data periods, changing model structure, using a more sophisticated objective function). A suite of relevant methods is available and the major useful ones are discussed here including sensitivity analysis, response surface methods, model emulation and the quantification of uncertainty. The paper also addresses various perspectives and concepts that warrant further development and use

Simulation support for performance assessment of building components

The determination of performance metrics for novel building components requires that the tests are conducted in the outdoor environment. It is usually difficult to do this when the components are located in a full-scale building because of the difficulty in controlling the experiments. Test cells allow the components to be tested in realistic, but controlled, conditions. High-quality outdoor experiments and identification analysis methods can be used to determine key parameters that quantify performance. This is important for achieving standardised metrics that characterise the building component of interest, whether it is a passive solar component such as a ventilated window, or an active component such as a hybrid photovoltaic module. However, such testing and analysis does not determine how the building component will perform when placed in a real building in a particular location and climate. For this, it is necessary to model the whole building with and without the building component of interest. A procedure has been developed, and applied within several major European projects, that consists of calibrating a simulation model with high-quality data from the outdoor tests and then applying scaling and replication to one or more buildings and locations to determine performance in practice of building components. This paper sets out the methodology that has been developed and applied in these European projects. A case study is included demonstrating its application to the performance evaluation of hybrid photovoltaic modules