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Authors: Ju Peng Poh, Jun Yu Charles Lee, Jonathan Chew Hoe Khoo

Abstract:

The emergence of digital twin technology, a digital replica of physical world, has improved the real-time access to data from sensors about the performance of buildings. This digital transformation has opened up many opportunities to improve the management of the building by using the data collected to help monitor consumption patterns and energy leakages. One example is the integration of predictive models for anomaly detection. In this paper, we use the GAM (Generalised Additive Model) for the anomaly detection of Air Handling Units (AHU) power consumption pattern. There is ample research work on the use of GAM for the prediction of power consumption at the office building and nation-wide level. However, there is limited illustration of its anomaly detection capabilities, prescriptive analytics case study, and its integration with the latest development of digital twin technology. In this paper, we applied the general GAM modelling framework on the historical data of the AHU power consumption and cooling load of the building between Jan 2018 to Aug 2019 from an education campus in Singapore to train prediction models that, in turn, yield predicted values and ranges. The historical data are seamlessly extracted from the digital twin for modelling purposes. We enhanced the utility of the GAM model by using it to power a real-time anomaly detection system based on the forward predicted ranges. The magnitude of deviation from the upper and lower bounds of the uncertainty intervals is used to inform and identify anomalous data points, all based on historical data, without explicit intervention from domain experts. Notwithstanding, the domain expert fits in through an optional feedback loop through which iterative data cleansing is performed. After an anomalously high or low level of power consumption detected, a set of rule-based conditions are evaluated in real-time to help determine the next course of action for the facilities manager. The performance of GAM is then compared with other approaches to evaluate its effectiveness. Lastly, we discuss the successfully deployment of this approach for the detection of anomalous power consumption pattern and illustrated with real-world use cases.

Keywords: Anomaly detection, digital twin, Generalised Additive Model, Power Consumption Model.

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Authors: Marijana Ivanov, Roman Šubić

Abstract:

This study analyzes the critical gaps in the architecture of European stability and the expected role of the banking union as the new important step towards completing the Economic and Monetary Union that should enable the creation of safe and sound financial sector for the euro area market. The single rulebook together with the Single Supervisory Mechanism and the Single Resolution Mechanism - as two main pillars of the banking union, should provide a consistent application of common rules and administrative standards for supervision, recovery and resolution of banks – with the final aim of replacing the former bail-out practice with the bail-in system through which possible future bank failures would be resolved by their own funds, i.e. with minimal costs for taxpayers and real economy. In this way, the vicious circle between banks and sovereigns would be broken. It would also reduce the financial fragmentation recorded in the years of crisis as the result of divergent behaviors in risk premium, lending activities and interest rates between the core and the periphery. In addition, it should strengthen the effectiveness of monetary transmission channels, in particular the credit channels and overflows of liquidity on the money market which, due to the fragmentation of the common financial market, has been significantly disabled in period of crisis. However, contrary to all the positive expectations related to the future functioning of the banking union, major findings of this study indicate that characteristics of the economic system in which the banking union will operate should not be ignored. The euro area is an integration of strong and weak entities with large differences in economic development, wealth, assets of banking systems, growth rates and accountability of fiscal policy. The analysis indicates that low and unbalanced economic growth remains a challenge for the maintenance of financial stability and this problem cannot be resolved just by a single supervision. In many countries bank assets exceed their GDP by several times and large banks are still a matter of concern, because of their systemic importance for individual countries and the euro zone as a whole. The creation of the Single Supervisory Mechanism and the Single Resolution Mechanism is a response to the European crisis, which has particularly affected peripheral countries and caused the associated loop between the banking crisis and the sovereign debt crisis, but has also influenced banks’ balance sheets in the core countries, as the result of crossborder capital flows. The creation of the SSM and the SRM should prevent the similar episodes to happen again and should also provide a new opportunity for strengthening of economic and financial systems of the peripheral countries. On the other hand, there is a potential threat that future focus of the ECB, resolution mechanism and other relevant institutions will be extremely oriented towards large and significant banks (whereby one half of them operate in the core and most important euro area countries), and therefore it remains questionable to what extent will the common resolution funds will be used for rescue of less important institutions. Recent geopolitical developments will be the optimal indicator to show whether the previously established mechanisms are sufficient enough to maintain the adequate financial stability in the euro area market.

Keywords: Banking Union, financial integration, single supervisory mechanism (SSM).

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Authors: Payam Mousavi, Andrew L. Stewart, Huiwen You, Aryeh F. G. Fayerman

Abstract:

We present a decision-support tool to assist an operator in the detection and tracking of a suspect vehicle traveling to an unknown target destination. Multiple data sources, such as traffic cameras, traffic information, weather, etc., are integrated and processed in real-time to infer a suspect’s intended destination chosen from a list of pre-determined high-value targets. Previously, we presented our work in the detection and tracking of vehicles using traffic and airborne cameras. Here, we focus on the fusion and processing of that information to predict a suspect’s behavior. The network of cameras is represented by a directional graph, where the edges correspond to direct road connections between the nodes and the edge weights are proportional to the average time it takes to travel from one node to another. For our experiments, we construct our graph based on the greater Los Angeles subset of the Caltrans’s “Performance Measurement System” (PeMS) dataset. We propose a Bayesian approach where a posterior probability for each target is continuously updated based on detections of the suspect in the live video feeds. Additionally, we introduce the concept of ‘soft interventions’, inspired by the field of Causal Inference. Soft interventions are herein defined as interventions that do not immediately interfere with the suspect’s movements; rather, a soft intervention may induce the suspect into making a new decision, ultimately making their intent more transparent. For example, a soft intervention could be temporarily closing a road a few blocks from the suspect’s current location, which may require the suspect to change their current course. The objective of these interventions is to gain the maximum amount of information about the suspect’s intent in the shortest possible time. Our system currently operates in a human-on-the-loop mode where at each step, a set of recommendations are presented to the operator to aid in decision-making. In principle, the system could operate autonomously, only prompting the operator for critical decisions, allowing the system to significantly scale up to larger areas and multiple suspects. Once the intended target is identified with sufficient confidence, the vehicle is reported to the authorities to take further action. Other recommendations include a selection of road closures, i.e., soft interventions, or to continue monitoring. We evaluate the performance of the proposed system using simulated scenarios where the suspect, starting at random locations, takes a noisy shortest path to their intended target. In all scenarios, the suspect’s intended target is unknown to our system. The decision thresholds are selected to maximize the chances of determining the suspect’s intended target in the minimum amount of time and with the smallest number of interventions. We conclude by discussing the limitations of our current approach to motivate a machine learning approach, based on reinforcement learning in order to relax some of the current limiting assumptions.

Keywords: Autonomous surveillance, Bayesian reasoning, decision-support, interventions, patterns-of-life, predictive analytics, predictive insights.

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