The Validation of Speech Corpora

1.2 Intended audience........................

Is Big Data Sufficient for a Reliable Detection of Non-Technical Losses?

Non-technical losses (NTL) occur during the distribution of electricity in power grids and include, but are not limited to, electricity theft and faulty meters. In emerging countries, they may range up to 40% of the total electricity distributed. In order to detect NTLs, machine learning methods are used that learn irregular consumption patterns from customer data and inspection results. The Big Data paradigm followed in modern machine learning reflects the desire of deriving better conclusions from simply analyzing more data, without the necessity of looking at theory and models. However, the sample of inspected customers may be biased, i.e. it does not represent the population of all customers. As a consequence, machine learning models trained on these inspection results are biased as well and therefore lead to unreliable predictions of whether customers cause NTL or not. In machine learning, this issue is called covariate shift and has not been addressed in the literature on NTL detection yet. In this work, we present a novel framework for quantifying and visualizing covariate shift. We apply it to a commercial data set from Brazil that consists of 3.6M customers and 820K inspection results. We show that some features have a stronger covariate shift than others, making predictions less reliable. In particular, previous inspections were focused on certain neighborhoods or customer classes and that they were not sufficiently spread among the population of customers. This framework is about to be deployed in a commercial product for NTL detection.Comment: Proceedings of the 19th International Conference on Intelligent System Applications to Power Systems (ISAP 2017

Reflections on Modern Macroeconomics: Can We Travel Along a Safer Road?

In this paper we sketch some reflections on the pitfalls and inconsistencies of the research program - currently dominant among the profession - aimed at providing microfoundations to macroeconomics along a Walrasian perspective. We argue that such a methodological approach constitutes an unsatisfactory answer to a well-posed research question, and that alternative promising routes have been long mapped out but only recently explored. In particular, we discuss a recent agent-based, truly non-Walrasian macroeconomic model, and we use it to envisage new challenges for future research.Comment: Latex2e v1.6; 17 pages with 4 figures; for inclusion in the APFA5 Proceeding

Preventing Atomicity Violations with Contracts

Software developers are expected to protect concurrent accesses to shared regions of memory with some mutual exclusion primitive that ensures atomicity properties to a sequence of program statements. This approach prevents data races but may fail to provide all necessary correctness properties.The composition of correlated atomic operations without further synchronization may cause atomicity violations. Atomic violations may be avoided by grouping the correlated atomic regions in a single larger atomic scope. Concurrent programs are particularly prone to atomicity violations when they use services provided by third party packages or modules, since the programmer may fail to identify which services are correlated. In this paper we propose to use contracts for concurrency, where the developer of a module writes a set of contract terms that specify which methods are correlated and must be executed in the same atomic scope. These contracts are then used to verify the correctness of the main program with respect to the usage of the module(s). If a contract is well defined and complete, and the main program respects it, then the program is safe from atomicity violations with respect to that module. We also propose a static analysis based methodology to verify contracts for concurrency that we applied to some real-world software packages. The bug we found in Tomcat 6.0 was immediately acknowledged and corrected by its development team

Reconstruction of Multidecadal Country-Aggregated Hydro Power Generation in Europe Based on a Random Forest Model

Hydro power can provide a source of dispatchable low-carbon electricity and a storage solution in a climate-dependent energy mix with high shares of wind and solar production. Therefore, understanding the effect climate has on hydro power generation is critical to ensure a stable energy supply, particularly at a continental scale. Here, we introduce a framework using climate data to model hydro power generation at the country level based on a machine learning method, the random forest model, to produce a publicly accessible hydro power dataset from 1979 to present for twelve European countries. In addition to producing a consistent European hydro power generation dataset covering the past 40 years, the specific novelty of this approach is to focus on the lagged effect of climate variability on hydro power. Specifically, multiple lagged values of temperature and precipitation are used. Overall, the model shows promising results, with the correlation values ranging between 0.85 and 0.98 for run-of-river and between 0.73 and 0.90 for reservoir-based generation. Compared to the more standard optimal lag approach the normalised mean absolute error reduces by an average of 10.23% and 5.99%, respectively. The model was also implemented over six Italian bidding zones to also test its skill at the sub-country scale. The model performance is only slightly degraded at the bidding zone level, but this also depends on the actual installed capacity, with higher capacities displaying higher performance. The framework and results presented could provide a useful reference for applications such as pan-European (continental) hydro power planning and for system adequacy and extreme events assessments

An Assessment of PIER Electric Grid Research 2003-2014 White Paper

This white paper describes the circumstances in California around the turn of the 21st century that led the California Energy Commission (CEC) to direct additional Public Interest Energy Research funds to address critical electric grid issues, especially those arising from integrating high penetrations of variable renewable generation with the electric grid. It contains an assessment of the beneficial science and technology advances of the resultant portfolio of electric grid research projects administered under the direction of the CEC by a competitively selected contractor, the University of California’s California Institute for Energy and the Environment, from 2003-2014

UK Research Information Shared Service (UKRISS) Final Report, July 2014

The reporting of research information is a complex and expensive activity for research organisations (ROs). There is little alignment between funders of the reporting requests made to institutions and requests made to individual researchers about their research outputs and outcomes. This inevitably results in duplication and increased costs across the sector, whilst limiting the potential sharing and reuse of the information. The UK Research Information Shared Service (UKRISS) project conducted a feasibility and scoping study for the reporting of research information at a national level based on CERIF (Common European Research Information Format), with the objective of increasing efficiency, productivity and quality across the sector. The aim was to define and prototype solutions which are compelling, easy to use, have a low entry barrier, and support innovative information sharing and benchmarking. CERIF has emerged as the preferred format for expressing research information across Europe. To date, CERIF has been piloted for specific applications, but not as a format for reporting requirements across all UK ROs. The final report presents the work carried out by the UKRISS project, including requirements gathering, modelling and prototyping, as well as recommendation for sustainability. UKRISS was divided into two phases. Phase 1, mapping the reporting landscape, ran from March 2012 to December 2012. Phase 2, exploring delivery of potential solutions, began in February 2013 and ended in December 2013

Business Groups as Hierarchies of Firms: Determinants of Vertical Integration and Performance

We explore the nature of Business Groups, that is network-like forms of hierarchical organization between legally autonomous firms spanning both within and across national borders. Exploiting a unique dataset of 270,474 headquarters controlling more than 1,500,000 (domestic and foreign) affiliates in all countries worldwide, we find that business groups account for a significant part of value-added generation in both developed and developing countries, with a prevalence in the latter. In order to characterize their boundaries, we distinguish between an affiliate vs. a group-level index of vertical integration, as well as an entropy-like metric able to summarize the hierarchical complexity of a group and its trade-off between exploitation of knowledge as an input across the hierarchy and the associated communication costs. We relate these metrics to host country institutional characteristics, as well as to the performance of affiliates across business groups. Conditional on institutional quality, a negative correlation exists between vertical integration and organizational complexity in defining the boundaries of business groups. We also find a robust (albeit non-linear) positive relationship between a group's organizational complexity and productivity which dominates the already known correlation between vertical integration and productivity. Results are in line with the theoretical framework of knowledge-based hierarchies developed by the literature, in which intangible assets are a complementary input in the production processes