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Open image in new window Fig. 1 Outline of the FGMaPR forecast concept.
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In the present contribution, a Big Data forecasting concept is described.
Fig. 1 Big Data energy forecasting infrastructure Fig. 2 Usage scenario of the Big Data forecasting concept.
Thereafter, the Big Data forecasting concept and the conducted benchmark are presented.
A usage scenario for the Big Data forecasting concept is illustrated in Fig. 2. First, the data analyst implements a forecasting model using the Zeppelin software.
In addition, the presented Big Data forecasting concept can serve in the development of Big Data forecasting tools, for example, in the Helmholtz Association's Energy System 2050 (ES 2050) project.2.
To this end, the present contribution offers a description of a Big Data forecasting concept that utilizes the distributed computing framework Apache Spark1 for the creation and application of forecasting models.
Data quality is a key issue with forecasting concepts.
ARIMAX and ANN forecasting concepts have been applied widely in different energy applications such as buildings, industrial loads and renewable energy [13, 14].
In TER experiment, the level of temperature is similar to the one forecast for concept.
In the present work a spatiotemporal model, based on climatic forecast using the concept of growing degree days [ 16], retrospectively predicts a latitudinal and altitudinal spreading of the potential transmission area of Dirofilaria, mainly in the Russian Federation and Ukraine, near the boundaries where temperatures are close to the threshold for extrinsic incubation of parasite larvae.
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