Optical variability of counterparts of ROSAT X-ray sources near the north ecliptic pole

Optical counterparts of X-ray selected sources to a large part consist of active galactic nuclei (AGN). A large proportion of these counterparts turns out to be optically variable, and because the timescales and manner of this variability can give important clues regarding the physical nature and structure of the counterparts, searching for variable AGN is a worthwhile undertaking. In order to perform such an optical variability survey, a subset of 167 X-ray sources in an area around the NEP was selected in this work from the X-ray catalogues derived from the ROSAT all-sky survey (RASS). Contemporaneous optical observations were available from a collaboration with the Karl-Schwarzschildt observatory (KSO) Tautenburg. A total of 89 plates were analyzed, covering an area of 35 degrees square around the NEP and spanning a timebase of 30 month ...thesi

Integration of FM and asset management expertise in digital 3D building models

Purpose: The research establishes a Conceptual Process Model (CPM) as shown in Figure 1 which shows how Facility Management (FM) and Asset Management (AM) know-how, 3D laser scanning and Building Information Modelling (BIM) can be combined with virtual design and simulation techniques to help managers make better decisions about feasibility report options and to add value and optimize existing buildings performance and quality. Design methodology and approach: Mixed methods were used including a review of BIM literature and industry best practice. Seven semi-structured interviews were held with stakeholders from different stages in the BIM process. The initial CPM was subsequently refined during the research project based on feedback from the interviews. The 3D laser scanning element of the CPM was tested using two ZHAW university buildings and the findings triangulated with a feedback mechanism to further improve the model. Originality and findings: The findings helped to develop a model which can be used by key stakeholders as a guide when considering the integration of FM and AM know-how, with 3D scanning in the creation of a BIM model for existing buildings, which constitute approximately 98% of the building stock. The focus is on combining existing know-how with the BIM process and simulation techniques to identify, simulate and evaluate the best building improvement options for feasibility reports prior to a decision to proceed. The CPM meets the need to develop a workflow with a focus on digitalisation of the existing built environment and creation of appropriate BIM model(s). The models can then be used for simulation purposes looking at cost benefit optimisation, energy efficiency, life cycle costing (LCC) etc. as well as creating virtual walk through models that can be viewed by end users, Facility Managers (FMs) and Asset Managers (AMs) to improve workplace environments and FM and AM operation

A Multi-Agent Reinforcement Learning Approach to Price and Comfort Optimization in HVAC-Systems

This paper addresses the challenge of minimizing training time for the control of Heating, Ventilation, and Air-conditioning (HVAC) systems with online Reinforcement Learning (RL). This is done by developing a novel approach to Multi-Agent Reinforcement Learning (MARL) to HVAC systems. In this paper, the environment formed by the HVAC system is formulated as a Markov Game (MG) in a general sum setting. The MARL algorithm is designed in a decentralized structure, where only relevant states are shared between agents, and actions are shared in a sequence, which are sensible from a system’s point of view. The simulation environment is a domestic house located in Denmark and designed to resemble an average house. The heat source in the house is an air-to-water heat pump, and the HVAC system is an Underfloor Heating system (UFH). The house is subjected to weather changes from a data set collected in Copenhagen in 2006, spanning the entire year except for June, July, and August, where heat is not required. It is shown that: (1) When comparing Single Agent Reinforcement Learning (SARL) and MARL, training time can be reduced by 70% for a four temperature-zone UFH system, (2) the agent can learn and generalize over seasons, (3) the cost of heating can be reduced by 19% or the equivalent to 750 kWh of electric energy per year for an average Danish domestic house compared to a traditional control method, and (4) oscillations in the room temperature can be reduced by 40% when comparing the RL control methods with a traditional control method