The integration of renewable sources, communication and power networks with information and communication technologies is one of the main challenges in Smart Grids (SG) large-scale testing. For this reason, the coupling of simulators is commonly used
to dynamically simulate several aspects of the SG infrastructure, in the so-called co-simulations. In this paper, we provide a scoping review of research of co-simulations in the context of Smart Grids: i) research areas and research problems addressed by co-simulations, ii) specific co-simulation aspects focus of research, iii) typical coupling of simulators in co-simulation studies. Based on the results, we discuss research directions of future SG co-simulation research in each of the identified areas.
In recent years, technologies of indoor crowd positioning and movement data analysis have received widespread attention in the fields of reliability management, indoor navigation, and crowd behavior monitoring. However, only a few indoor crowd moveme
nt trajectory datasets are available to the public, thus restricting the development of related research and application. This paper contributes a new benchmark dataset of indoor crowd movement trajectories. This dataset records the movements of over 5000 participants at a three day large academic conference in a two story indoor venue. The conference comprises varied activities, such as academic seminars, business exhibitions, a hacking contest, interviews, tea breaks, and a banquet. The participants are divided into seven types according to participation permission to the activities. Some of them are involved in anomalous events, such as loss of items, unauthorized accesses, and equipment failures, forming a variety of spatial temporal movement patterns. In this paper, we first introduce the scenario design, entity and behavior modeling, and data generator of the dataset. Then, a detailed ground truth of the dataset is presented. Finally, we describe the process and experience of applying the dataset to the contest of ChinaVis Data Challenge 2019. Evaluation results of the 75 contest entries and the feedback from 359 contestants demonstrate that the dataset has satisfactory completeness, and usability, and can effectively identify the performance of methods, technologies, and systems for indoor trajectory analysis.
In this paper, authors construct a new type of sequence which is named an extra-super increasing sequence, and give the definitions of the minimal super increasing sequence {a[0], a[1], ..., a[n]} and minimal extra-super increasing sequence {z[0], z[
1], ..., z[n]}. Find that there always exists a fit n which makes (z[n] / z[n-1] - a[n] / a[n-1])= PHI, where PHI is the golden ratio conjugate with a finite precision in the range of computer expression. Further, derive the formula radic(5) = 2(z[n] / z[n-1] - a[n] / a[n-1]) + 1, where n corresponds to the demanded precision. Experiments demonstrate that the approach to radic(5) through a term ratio difference is more smooth and expeditious than through a Taylor power series, and convince the authors that lim(n to infinity) (z[n] / z[n-1] - a[n] / a[n-1]) = PHI holds.
A trajectory, defined as a sequence of location measurements, contains valuable information about movements of an individual. Its value of information (VOI) may change depending on the specific application. However, in a variety of applications, know
ing the intrinsic VOI of a trajectory is important to guide other subsequent tasks or decisions. This work aims to find a principled framework to quantify the intrinsic VOI of trajectories from the owners perspective. This is a challenging problem because an appropriate framework needs to take into account various characteristics of the trajectory, prior knowledge, and different types of trajectory degradation. We propose a framework based on information gain (IG) as a principled approach to solve this problem. Our IG framework transforms a trajectory with discrete-time measurements to a canonical representation, i.e., continuous in time with continuous mean and variance estimates, and then quantifies the reduction of uncertainty about the locations of the owner over a period of time as the VOI of the trajectory. Qualitative and extensive quantitative evaluation show that the IG framework is capable of effectively capturing important characteristics contributing to the VOI of trajectories.
Freight carriers rely on tactical plans to satisfy demand in a cost-effective way. For computational tractability in real large-scale settings, such plans are typically computed by solving deterministic and cyclic formulations. An important input is
the periodic demand, i.e., the demand that is expected to repeat in each period of the planning horizon. Motivated by the discrepancy between time series forecasts of demand in each period and the periodic demand, Laage et al. (2021) recently introduced the Periodic Demand Estimation (PDE) problem and showed that it has a high value. However, they made strong assumptions on the solution space so that the problem could be solved by enumeration. In this paper we significantly extend their work. We propose a new PDE formulation that relaxes the strong assumptions on the solution space. We solve large instances of this formulation with a two-step heuristic. The first step reduces the dimension of the feasible space by performing clustering of commodities based on instance-specific information about demand and supply interactions. The formulation along with the first step allow to solve the problem in a second step by either metaheuristics or the state-of-the-art black-box optimization solver NOMAD. In an extensive empirical study using real data from the Canadian National Railway Company, we show that our methodology produces high quality solutions and outperforms existing ones.
Building metadata is regarded as the signpost in organizing massive building data. The application of building metadata simplifies the creation of digital representations and provides portable data analytics. Typical metadata standards such as Brick
and Haystack are used to describe the data of the building system. Brick uses standard ontologies to create building metadata. However, neither Haystack nor Brick has provided definitions about the Variable Refrigerant Flow (VRF) system so far. For years, both Brick and Haystack working groups have been discussing how to describe VRF in their schema, mainly about the classification of VRF and the definitions of VRF units. There were no settled solutions for these problems. Meanwhile, the global VRF market is growing increasingly fast because of the energy efficiency and installation simplicity of the VRF system. It is needed to have the metadata to describe VRF units in buildings for data analysis and management. Addressing this challenge, this paper extended Brick Schema with the VRF module and verified the Brick VRF module. Then, the model and the service framework were developed and applied for a building in China. The framework can serve portable energy analysis for different areas. The VRF module of this paper provides a possible solution for the expression of the VRF system in the building semantic web. The works in this paper will support semantic web in automation strategies for building management and scalable building operation.
Structural analysis is a method for verifying equation-oriented models in the design of industrial systems. Existing structural analysis methods need flattening the hierarchical models into an equation system for analysis. However, the large-scale eq
uations in complex models make the structural analysis difficult. Aimed to address the issue, this study proposes a hierarchical structural analysis method by exploring the relationship between the singularities of the hierarchical equation-oriented model and its components. This method obtains the singularity of a hierarchical equation-oriented model by analyzing the dummy model constructed with the parts from the decomposing results of its components. Based on this, the structural singularity of a complex model can be obtained by layer-by-layer analysis according to their natural hierarchy. The hierarchical structural analysis method can reduce the equation scale in each analysis and achieve efficient structural analysis of very complex models. This method can be adaptively applied to nonlinear algebraic and differential-algebraic equation models. The main algorithms, application cases, and comparison with the existing methods are present in the paper. Complexity analysis results show the enhanced efficiency of the proposed method in structural analysis of complex equation-oriented models. As compared with the existing methods, the time complexity of the proposed method is improved significantly.
Data Science advances in sports commonly involve big data, i.e., large sport-related data sets. However, such big data sets are not always available, necessitating specialized models that apply to relatively few observations. One important area of sp
ort-science research that features small data sets is the study of energy recovery from exercise. In this area, models are typically fitted to data collected from exhaustive exercise test protocols, which athletes can perform only a few times. Recent findings highlight that established recovery models like W balance (Wbal) models are too simple to adequately fit observed trends in the data. Therefore, we investigated a hydraulic model that requires the same few data points as Wbal models to be applied, but promises to predict recovery dynamics more accurately. To compare the hydraulic model to established Wbal models, we retrospectively applied them to a compilation of data from published studies. In total, one hydraulic model and three Wbal models were compared on data extracted from five studies. The hydraulic model outperformed established Wbal models on all defined metrics, even those that penalize models featuring higher numbers of parameters. These results incentivize further investigation of the hydraulic model as a new alternative to established performance models of energy recovery.
We consider the problem of maximizing the number of people that a dining room can accommodate provided that the chairs belonging to different tables are socially distant. We introduce an optimization model that incorporates several characteristics of
the problem, namely: the type and size of surface of the dining room, the shapes and sizes of the tables, the positions of the chairs, the sitting sense of the customers, and the possibility of adding space separators to increase the capacity. We propose a simple yet general set-packing formulation for the problem. We investigate the efficiency of space separators and the impact of considering the sitting sense of customers in the room capacity. We also perform an algorithmic analysis of the model, and assess its scalability to the problem size, the presence of (or lack thereof) room separators, and the consideration of the sitting sense of customers.
The GDMC AI settlement generation challenge is a PCG competition about producing an algorithm that can create an interesting Minecraft settlement for a given map. This paper contains a collection of written experiences with this competition, by parti
cipants, judges, organizers and advisors. We asked people to reflect both on the artifacts themselves, and on the competition in general. The aim of this paper is to offer a shareable and edited collection of experiences and qualitative feedback - which seem to contain a lot of insights on PCG and computational creativity, but would otherwise be lost once the output of the competition is reduced to scalar performance values. We reflect upon some organizational issues for AI competitions, and discuss the future of the GDMC competition.
