The transfer of knowledge has not changed fundamentally for many hundreds of years: It is usually document-based-formerly printed on paper as a classic essay and nowadays as PDF. With around 2.5 million new research contributions every year, researchers drown in a flood of pseudo-digitized PDF publications. As a result research is seriously weakened. In this article, we argue for representing scholarly contributions in a structured and semantic way as a knowledge graph. The advantage is that information represented in a knowledge graph is readable by machines and humans. As an example, we give an overview on the Open Research Knowledge Graph (ORKG), a service implementing this approach. For creating the knowledge graph representation, we rely on a mixture of manual (crowd/expert sourcing) and (semi-)automated techniques. Only with such a combination of human and machine intelligence, we can achieve the required quality of the representation to allow for novel exploration and assistance services for researchers. As a result, a scholarly knowledge graph such as the ORKG can be used to give a condensed overview on the state-of-the-art addressing a particular research quest, for example as a tabular comparison of contributions according to various characteristics of the approaches. Further possible intuitive access interfaces to such scholarly knowledge graphs include domain-specific (chart) visualizations or answering of natural language questions.
The Open Research Knowledge Graph is an infrastructure for the production, curation, publication and use of FAIR scientific information. Its mission is to shape a future scholarly publishing and communication where the contents of scholarly articles are FAIR research data.
Knowing that electrical load is a non storable resource; short term electric load forecasting becomes an important tool to optimise dispatching of electrical load in regular system planning. Several techniques have been used to accomplish this task, from traditional linear regression and Box-Jenkins to artificial intelligence approaches such as Artificial Neural Networks (ANN). This work presents a comparative study of serial and parallel ANN approaches for forecasting 168 hours ahead using a multiple linear regression model as a benchmark for comparison. The results obtained by the latter method, are compared with the ANN serial and parallel developed approaches. These models were trained solemnly on past load consumption data, given by the Algerian national electricity company. This results in Nonlinear Autoregressive Models (NAR), however once the approach validity is proven, the addition of exogenous inputs can only improve model results.
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