pwc-archive/papers-with-abstracts huggingface dataset:
Regression-Based Model Error Compensation for Hierarchical MPC Building Energy Management System
Authors: Thomas Schmitt, Jens Engel, Tobias Rodemann
Date: 2023-06-15 00:00:00
Tasks: energy management, Management, Model Predictive Control, regression

One of the major challenges in the development of energy management systems (EMSs) for complex buildings is accurate modeling. To address this, we propose an EMS, which combines a Model Predictive Control (MPC) approach with data-driven model error compensation. The hierarchical MPC approach consists of two layers: An aggregator controls the overall energy flows of the building in an aggregated perspective, while a distributor distributes heating and cooling powers to individual temperature zones. The controllers of both layers employ regression-based error estimation to predict and incorporate the model error. The proposed approach is evaluated in a software-in-the-loop simulation using a physics-based digital twin model. Simulation results show the efficacy and robustness of the proposed approach
Multi-objective path tracking control for car-like vehicles with differentially bounded n-smooth output
Authors: Karin Festl, Michael Stolz, Daniel Watzenig
Date: 2023-06-15 00:00:00

When designing path tracking controllers for car-like vehicles, two main aspects are the tracking performance and the characteristics of the actuation signal. Our work bases on an existing variable structure controller that was designed with the geometrically optimal solution of a Dubins car, but with chattering on the output. In this contribution, we extend this approach to achieve an actuation signal that is n-smooth and differentially bounded. While the global stability under matched disturbances is maintained, the finite time reaching behavior is exchanged for asymptotic convergence. With the $n$-smooth output, n new parameters are introduced, weighing the control characteristics between the tracking performance and the magnitude of the steering angles derivatives. The controller is also evaluated in simulation, demonstrating the tuning capabilities, as well as the reaching and tracking behavior. The main contribution of this work is a control law designed to produce a smooth steering angle with implicit satisfaction of bounds on its derivatives.
Plug-in Hybrid Electric Vehicle Energy Management with Clutch Engagement Control via Continuous-Discrete Reinforcement Learning
Authors: Changfu Gong, Jinming Xu, Yuan Lin
Date: 2023-06-15 00:00:00
Tasks: energy management, Management, reinforcement-learning

Energy management strategy (EMS) is a key technology for plug-in hybrid electric vehicles (PHEVs). The energy management of certain series-parallel PHEVs involves the control of continuous variables, such as engine torque, and discrete variables, such as clutch engagement/disengagement. We establish a control-oriented model for a series-parallel plug-in hybrid system with clutch engagement control from the perspective of mixed-integer programming. Subsequently, we design an EMS based on continuous-discrete reinforcement learning (CDRL), which enables simultaneous output of continuous and discrete variables. During training, we introduce state-of-charge (SOC) randomization to ensure that the hybrid system exhibits optimal energy-saving performance in both high and low SOC. Finally, the effectiveness of the proposed CDRL strategy is verified by comparing EMS based on charge-depleting charge-sustaining (CD-CS) with rule-based clutch engagement control, and Dynamic Programming (DP). The simulation results show that, under a high SOC, the CDRL strategy proposed in this paper can improve energy efficiency by 8.3% compared to CD-CS, and the energy consumption is just 6.6% higher than the global optimum based on DP, while under a low SOC, the numbers are 4.1% and 3.9%, respectively.
Learning to Stabilize High-dimensional Unknown Systems Using Lyapunov-guided Exploration
Authors: Songyuan Zhang, Chuchu Fan
Date: 2023-06-14 00:00:00
Tasks: Imitation Learning

Designing stabilizing controllers is a fundamental challenge in autonomous systems, particularly for high-dimensional, nonlinear systems that can hardly be accurately modeled with differential equations. The Lyapunov theory offers a solution for stabilizing control systems, still, current methods relying on Lyapunov functions require access to complete dynamics or samples of system executions throughout the entire state space. Consequently, they are impractical for high-dimensional systems. This paper introduces a novel framework, LYapunov-Guided Exploration (LYGE), for learning stabilizing controllers tailored to high-dimensional, unknown systems. LYGE employs Lyapunov theory to iteratively guide the search for samples during exploration while simultaneously learning the local system dynamics, control policy, and Lyapunov functions. We demonstrate its scalability on highly complex systems, including a high-fidelity F-16 jet model featuring a 16D state space and a 4D input space. Experiments indicate that, compared to prior works in reinforcement learning, imitation learning, and neural certificates, LYGE reduces the distance to the goal by 50% while requiring only 5% to 32% of the samples. Furthermore, we demonstrate that our algorithm can be extended to learn controllers guided by other certificate functions for unknown systems.
Coordinating Distributed Energy Resources for Reliability can Significantly Reduce Future Distribution Grid Upgrades and Peak Load
Authors: Thomas Navidi, Abbas El Gamal, Ram Rajagopal
Date: 2023-06-14 00:00:00

Current DER coordination schemes, such as demand-response and VPPs, aim to reduce electricity costs during peak demand events with no consideration of distribution grid reliability. We show that coordinating DERs for grid reliability can significantly reduce both the infrastructure upgrades needed to support future increases in DER and electrification penetrations and peak load. Specifically, using a power flow driven simulation-optimization methodology, we compare the potential reliability improvements with a perfect-foresight centralized DER controller that minimizes reliability violations to a local controller that minimizes consumer electricity cost. We find, for example, that by 2050 with local control, on average 81% of the transformers in a distribution grid experience violations, compared to 28% with centralized control, which simultaneously reduces peak load by 17%. These reductions are achieved with only 5.1% increase in electricity cost. These findings suggest that future incentives for DER adoption should include reliability coordination.
Transmission and Distribution Coordination for DER-rich Energy Markets: A Parametric Programming Approach
Authors: Mohammad Mousavi, Meng Wu
Date: 2023-06-14 00:00:00

In this paper, a framework is proposed to coordinate the operation of the independent system operator (ISO) and distribution system operator (DSO). The framework is compatible with current practice of the U.S. wholesale market to enable massive distributed energy resources (DERs) to participate in the wholesale market. The DSO builds a bid-in cost function to be submitted to the ISO market through parametric programming. Once the ISO clears the wholesale market, the dispatch and payment of the DSO are determined by ISO. Then, the DSO determines the dispatch and payment of the DER aggregators. To compare the proposed framework, an ideal case is defined in which DER aggregators can participate in the wholesale market directly and ISO overseas operation of both transmission and distribution systems. We proved 1) the dispatches of the proposed ISO-DSO coordination framework are identical to those of the ideal case; 2) the payments to each DER aggregator are identical in the proposed framework and in the ideal case. Case studies are performed on a small illustrative example as well as a large test system which includes IEEE 118 bus transmission system and two distribution systems - the IEEE 33 node and IEEE 240 node test systems.
Optimisation of Nonlinear Spring and Damper Characteristics for Vehicle Ride and Handling Improvement
Authors: D. Ozcan, U. Sonmez, L. Guvenc, S . S. Ersolmaz, I. Y. Eyol
Date: 2023-06-14 00:00:00

In this paper, the optimum linear/nonlinear spring and linear/nonlinear damper force versus displacement and force versus velocity characteristic functions, respectively, are determined using simple lumped parameter models of a quarter car front independent suspension and a half car rear solid axle suspension of a light commercial vehicle. The complexity of a nonlinear function optimisation problem is reduced by determining the shape a priori based on typical shapes supplied by the car manufacturer and then scaling it up or down in the optimisation process. The vehicle ride and handling responses are investigated considering models of increased complexity. The linear and nonlinear optimised spring characteristics are first obtained using lower complexity lumped parameter models. The commercial vehicle dynamics software Carmaker is then used in the optimisation as the higher complexity, more realistic model. The performance of the optimised suspension units are also verified using this more realistic Carmaker model.
Cooperative Adaptive Cruise Control Design and Implementation
Authors: Mustafa Ridvan Cantas, Sukru Yaren Gelbal, Levent Guvenc, Bilin Aksun Guvenc
Date: 2023-06-14 00:00:00

In this manuscript a design and implementation of CACC on an autonomous vehicle platform (2017 Ford Fusion) is presented. The developed CACC controls the intervehicle distance between the target vehicle and ego vehicle using a feedforward PD controller. In this design the feedforward information is the acceleration of the target vehicle which is communicated through Dedicated Short-Range Communication (DSRC) modem. The manuscript explains the detailed architecture of the designed CACC with used hardware and methods for the both simulation and experiments. Also, an approach to overcome detection failures at the curved roads is presented to improve overall quality of the designed CACC system. As a result, the initial simulation and experimental results with the designed CACC system is presented in the paper. The presented results indicate that CACC improves the car following performance of the ego vehicle as compared to the classical Adaptive Cruise Controller.
An Interval Arithmetic Approach to Input-Output Reachability
Authors: Ivan Perez Avellaneda, Luis A. Duffaut Espinosa
Date: 2023-06-13 00:00:00

The safety region of operation of a system is the subset of allowed outputs for which no undesirable outcome would occur. Knowing if a system would ever leave its safety regions of operation is important information for the planning and control of systems. The computation of the set of outputs also known as the reachable set of the system is, in many cases, intensive and a simple overestimation of it is preferred, instead. There are several perspectives to address this problem including set-based approaches, Mixed-Monotonicity, Hamilton-Jacobi reachability, neural networks, and recently reachability via Chen-Fliess series. In the present work, a Chen-Fliess series representing the input-output behavior of a dynamical system along with interval arithmetic is used to overestimate the reachable set of a system. The advantage of this combination of techniques is that it provides a closed-form of the overestimating set. Examples are presented to illustrate the results.
Locality of contacts determines the subdiffusion exponents in polymeric models of chromatin
Authors: E. Marchi, Y. Zhan, G. Tiana
Date: 2023-06-14 00:00:00

Loop extrusion by motor proteins mediates the attractive interactions in chromatin on the length scale of megabases, providing the polymer with a well-defined structure and at the same time determining its dynamics. The mean square displacement of chromatin loci varies from a Rouse-like scaling to a more constrained subdiffusion, depending on cell type, genomic region and time scale. With a simple polymeric model, we show that such a Rouse-like dynamics occurs when the parameters of the model are chosen so that contacts are local along the chain, while in presence of non-local contacts, we observe subdiffusion at short time scales with exponents smaller than 0.5. Such exponents are independent of the detailed choice of the parameters and build a master curve that depends only on the mean locality of the resulting contacts. We compare the loop-extrusion model with a polymeric model with static links, showing that also in this case only the presence of nonlocal contacts can produce low-exponent subdiffusion. We interpret these results in terms of a simple analytical model.
Modelling and Forecasting Macroeconomic Risk with Time Varying Skewness Stochastic Volatility Models
Authors: Andrea Renzetti
Date: 2023-06-15 00:00:00
Tasks: quantile regression, regression

Monitoring downside risk and upside risk to the key macroeconomic indicators is critical for effective policymaking aimed at maintaining economic stability. In this paper I propose a parametric framework for modelling and forecasting macroeconomic risk based on stochastic volatility models with Skew-Normal and Skew-t shocks featuring time varying skewness. Exploiting a mixture stochastic representation of the Skew-Normal and Skew-t random variables, in the paper I develop efficient posterior simulation samplers for Bayesian estimation of both univariate and VAR models of this type. In an application, I use the models to predict downside risk to GDP growth in the US and I show that these models represent a competitive alternative to semi-parametric approaches such as quantile regression. Finally, estimating a medium scale VAR on US data I show that time varying skewness is a relevant feature of macroeconomic and financial shocks.
Inference in IV models with clustered dependence, many instruments and weak identification
Authors: Johannes W. Ligtenberg
Date: 2023-06-14 00:00:00

Data clustering reduces the effective sample size from the number of observations towards the number of clusters. For instrumental variable models I show that this reduced effective sample size makes the instruments more likely to be weak, in the sense that they contain little information about the endogenous regressor, and many, in the sense that their number is large compared to the sample size. Clustered data therefore increases the need for many and weak instrument robust tests. However, none of the previously developed many and weak instrument robust tests can be applied to this type of data as they all require independent observations. I therefore adapt two types of such tests to clustered data. First, I derive cluster jackknife Anderson-Rubin and score tests by removing clusters rather than individual observations from the statistics. Second, I propose a cluster many instrument Anderson-Rubin test which improves on the first type of tests by using a more optimal, but more complex, weighting matrix. I show that if the clusters satisfy an invariance assumption the higher complexity poses no problems. By revisiting a study on the effect of queenly reign on war I show the empirical relevance of the new tests.
Machine Learning for Zombie Hunting: Predicting Distress from Firms' Accounts and Missing Values
Authors: Falco J. Bargagli-Stoffi, Fabio Incerti, Massimo Riccaboni, Armando Rungi
Date: 2023-06-13 00:00:00
Tasks: Missing Values

In this contribution, we propose machine learning techniques to predict zombie firms. First, we derive the risk of failure by training and testing our algorithms on disclosed financial information and non-random missing values of 304,906 firms active in Italy from 2008 to 2017. Then, we spot the highest financial distress conditional on predictions that lies above a threshold for which a combination of false positive rate (false prediction of firm failure) and false negative rate (false prediction of active firms) is minimized. Therefore, we identify zombies as firms that persist in a state of financial distress, i.e., their forecasts fall into the risk category above the threshold for at least three consecutive years. For our purpose, we implement a gradient boosting algorithm (XGBoost) that exploits information about missing values. The inclusion of missing values in our predictive model is crucial because patterns of undisclosed accounts are correlated with firm failure. Finally, we show that our preferred machine learning algorithm outperforms (i) proxy models such as Z-scores and the Distance-to-Default, (ii) traditional econometric methods, and (iii) other widely used machine learning techniques. We provide evidence that zombies are on average less productive and smaller, and that they tend to increase in times of crisis. Finally, we argue that our application can help financial institutions and public authorities design evidence-based policies-e.g., optimal bankruptcy laws and information disclosure policies.
Migrant Laborer's Optimization Mechanism Under Employment Permit System(EPS): Introducing and Analyzing 'Skill-Relevance-Self Selection' Model
Authors: Kwonhyung Lee, Yejin Lim, Sunghyun Cho
Date: 2023-06-15 00:00:00
Tasks: Jurisprudence, Sociology

Migrant laborers subject to ROK's Employment Permit System(EPS) must strike a balance between host country's high wage and 'Depreciation of skill-relevance entailed by immigration', whilst taking account of the 'migration costs'. This study modelizes the optimization mechanism of migrant workers and the firms hiring them -- then induces the solution of the very model, namely, 'Subgame Perfect Nash Equilibrium(SPNE)', by utilizing game theory's 'backward induction' method. Analyzing the dynamics between variables at SPNE state, the attained stylized facts are what as follows; [1]Host nation's skill-relevance and wage differential have positive correlation. [2]Emigrating nation's skill-relevance and wage differential have negative correlation. Both stylized facts -- [1,2] -- are operationalized into 'Host nation skill-relevance hypothesis(H1)' and 'Emigrating nation skill-relevance hypothesis(H2)', respectively; of which are thoroughly tested by OLS linear regression analysis. In all sex/gender parameters(Total/Men/Women), test results support both hypotheses with statistical significance, thereby inductively substantiating the constructed model. This paper contributes to existing labor immigration literature in three following aspects: (1)Stimulate the economic approach to migrant labor analysis, and by such means, break away from the overflow of sociology, anthropology, political science, and jurisprudence in prior studies; (2)Shed a light on the EPS's microeconomic interaction process, of which was left undisclosed as a 'black box'; (3)Seek a complementary synthesis of two grand strands of research methodology -- that is, deductive modeling and inductive statistics.
Local Labor Market Effects of Mergers and Acquisitions in Developing Countries: Evidence from Brazil
Authors: Vitor Costa
Date: 2023-06-15 00:00:00

I use matched employer-employee records merged with corporate tax information from 2003 to 2017 to estimate labor market-wide effects of mergers and acquisitions in Brazil. Labor markets are defined by pairs of commuting zone and industry sector. In the following year of a merger, market size falls by 10.8%. The employment adjustment is concentrated in merging firms. For the firms not involved in M&As, I estimate a 1.07% decline in workers earnings and a positive, although not significant, increase in their size. Most mergers have a predicted impact of zero points in concentration, measured by the Herfindahl-Hirschman Index (HHI). I spillover firms, earnings decline similarly for mergers with high and low predicted changes in HHI. Contrary to the recent literature on market concentration in developed economies, I find no evidence of oligopsonistic behavior in Brazilian labor markets.
Do Productivity Shocks Cause Inputs Misallocation?
Authors: Davide Luparello
Date: 2023-06-14 00:00:00

Firms exhibit varying productivity levels even within narrowly defined industries and face uncertainty when predicting future performance. This paper investigates the link between productivity uncertainty, heterogeneity, and misallocation across all inputs. Using a model where heterogeneous firms face staggered productivity shocks, creating gaps between expected and actual productivity, I find a positive association between marginal revenue product dispersions and productivity variability. The analysis reveals that productivity shocks predominantly drive marginal revenue product dispersions. By comparing baseline estimates with those from the factor shares approach, I highlight the limitations of the latter method in analyzing the effects of productivity evolution.
Germany's nationwide travel experiment in 2022: public transport for 9 Euro per month -- First findings of an empirical study
Authors: Allister Loder, Fabienne Cantner, Lennart Adenaw, Nico Nachtigall, David Ziegler, Felix Gotzler, Markus B. Siewert, Stefan Wurster, Sebastian Goerg, Markus Lienkamp, Klaus Bogenberger
Date: 2023-06-14 00:00:00

In spring 2022, the German federal government agreed on a set of policy measures that aimed at reducing households' financial burden resulting from a recent price increase, especially in energy and mobility. These included among others, a nationwide public transport ticket for 9~Euro per month for three months in June, July, and August 2022. In transport policy research this is an almost unprecedented behavioral experiment. It allows us to study not only behavioral responses in mode choice and induced demand but also to assess the effectiveness of these instruments. We observe this natural experiment with a three-wave survey and a smartphone-based travel diary with passive tracking on an initial sample of 2,261 participants with a focus on the Munich metropolitan region. This area is chosen as it offers a variety of mode options with a dense and far-reaching public transport network that even provides good access to many leisure destinations. The app has been providing data from 756 participants until the end of September, the three-wave survey by 1,402, and the app and the three waves by 637 participants. In this paper, we report on the study design, the recruitment and study participation as well as the impacts of the policy measures on the self-reported and app-observed travel behavior; we present results on consumer choices for a successor ticket to the 9-Euro-Ticket that started in May 2023. We find a substantial shift in the modal share towards public transport from the car in our sample during the 9-Euro-Ticket period in travel distance (around 5 %) and in trip frequency (around 7 %). The mobility outcomes of the 9-Euro-Ticket however provide evidence that cheap public transport as a policy instrument does not suffice to incentive sustainable travel behavior choices and that other policy instruments are required in addition.