Data Analysis Solar Panels

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Manu Fourneau

Business Process Management Data Analysis

Données & analyses

Data Analysis
DASHBOARD SLIDE CAST RAPPORT

Context
 Howest Kortrijk
 NMCT
 BPM
 Interpreting, analysing and presenting big data

KPI
 Specific Objective: Be in dependable for power needs
 Measure: Measure kWh over time
 Attainable: By increasing number of Yingli tracking solar systems
 Time-Frame: 5 years

Interpretation
 Yingli Trackers Most Valuable
 Yingli Open Systems Most Reliable

Personal Conclusion
 Data Is vital
 Analysis is everything
 Correct interpretation can lead to amazing results

Contenu connexe

Tendances

Invited talk for 2016 AGU Fall Meeting Session IN12A Big Data Analytics I Introduced is a new approach for processing spatiotemporal big data by leveraging distributed analytics and storage. A suite of temporally-aware analysis tools summarizes data nearby or within variable windows, aggregates points (e.g., for various sensor observations or vessel positions), reconstructs time-enabled points into tracks (e.g., for mapping and visualizing storm tracks), joins features (e.g., to find associations between features based on attributes, spatial relationships, temporal relationships or all three simultaneously), calculates point densities, finds hot spots (e.g., in species distributions), and creates space-time slices and cubes (e.g., in microweather applications with temperature, humidity, and pressure, or within human mobility studies). These “feature geo analytics” tools run in both batch and streaming spatial analysis mode as distributed computations across a cluster of servers on typical “big” data sets, where static data exist in traditional geospatial formats (e.g., shapefile) locally on a disk or file share, attached as static spatiotemporal big data stores, or streamed in near-real-time. In other words, the approach registers large datasets or data stores with ArcGIS Server, then distributes analysis across a cluster of machines for parallel processing. Several brief use cases will be highlighted based on a 16-node server cluster at 14 Gb RAM per node, allowing, for example, the buffering of over 8 million points or thousands of polygons in ~1 minute. The approach is “hybrid” in that ArcGIS Server integrates open-source big data frameworks such as Apache Hadoop and Apache Spark on the cluster in order to run the analytics. In addition, the user may devise and connect custom open-source interfaces and tools developed in Python or Python Notebooks; the common denominator being the familiar REST API.

Feature Geo Analytics and Big Data Processing: Hybrid Approaches for Earth Sc...

Dawn Wright

Jiit 2013 14 project presentation aniket mishra

Aniket Mishra

Iot data aggrigation

dokechin

02 sandia pv modeling conference 9 may2017

Sandia National Laboratories: Energy & Climate: Renewables

KatRisk RAA 2018 Highlights

Dag Lohmann

Big data, Cloud, and the NOAA CRADA at The Climate Corporation

Valliappa Lakshmanan

Fine particulate matter (PM2.5) is a mixture of air pollutants that, at a high concentration level, has adverse effects on human health. An interesting statistics problem is to estimate these pollutant exposures for the entire US, such estimates can be used to inform policy and decision making. During the workshop, we will work on two major source of air quality data that are used by the EPA to estimate pollutant exposures, including monitoring data and the Community Multiscale Air Quality (CMAQ) model. The monitoring stations provide fairly accurate measurements of the pollutants; however, they are sparse in space and take measurements at a coarse time resolution, typically 1-in-3 or 1-in-6 days. On the other hand, the CMAQ model provides daily concentration levels of each component with complete spatial coverage on a grid; these model outputs, however, need to be evaluated and calibrated to the monitoring data. We will explore these air quality data for the summer of 2011 and brainstorm on statistical models to estimate air pollutant exposures. Group members: Meixi Chen, Vincent Gonzales, Alan Ji, Chandni Malhotra, Hongyu Mao, Sharon Sung

Undergraduate Modeling Workshop - Air Quality Working Group Final Presentatio...

The Statistical and Applied Mathematical Sciences Institute

A key challenge for smart city analytics is fast extraction, accumulation and processing of sensor data collected from a large number of IoT devices. Edge computing has enabled processing of simple analytics, such as aggregation, geographically closer to the IoT devices to improve latency. However, the throughput of processing in the edge depends on the type of resources available, the number of IoT devices connected and the type of stream analytics performed in the edge. We introduce a framework called Seagull for building efficient, large scale IoT-based applications. Our framework distributes the stream analytics processing tasks to the nodes based on their proximity to the sensor data source as well as the amount of processing the nodes can handle. Our evaluation shows the effect of various stream analytics parameters on the maximum sustainable throughput for a resource-constrained edge device.

Large Scale Stream Analytics using a Resource-constrained Edge

Gabriele Di Bernardo

MAP delivers advanced energy management capabilities by giving users the ability to monitor the performance of their most energy-intensive processes in a building or site such as; pumping, refrigeration, ventilation. MAP can deliver the key metrics to identify process inefficiencies as well as modelling complex electricity tariffs, providing real-time energy metrics and much more. www.meniscus.co.uk

Meniscus Advanced Energy Analytics Platform

Mike Everest

We present I², an interactive development environment for real-time analysis pipelines, which is based on Apache Flink and Apache Zeppelin. The sheer amount of available streaming data frequently makes it impossible to visualize all data points at the same time. I² coordinates running Flink jobs and corresponding visualizations such that only the currently depicted data points are processed in Flink and transferred towards the front end. We show how Flink jobs can adapt to changed visualization properties at runtime to allow interactive data exploration on high bandwidth data streams. Moreover, we present a data reduction technique which minimizes data transfer while providing loss free time-series plots. We show I² in a live demonstration in which we replay recorded sensor data from a football match (ca. 12k event/s). I² was first presented at EDBT'17 where it was awarded as best demonstration. The demonstration is available as open source at github.com/TU-Berlin-DIMA/i2.

I²: Interactive Real-Time Visualization for Streaming Data with Apache Flink ...

Jonas Traub

NOAA's Climate Data Record (CDR) Program

American Astronautical Society

In this talk we will look at how to efficiently (in both space and time) summarize large, potentially unbounded, streams of data by approximating the underlying distribution using so-called sketch algorithms. The main approach we are going to be looking at is summarization via histograms. Histograms have a number of desirable properties: they work well in an on-line setting, are embarrassingly parallel, and are space-bound. Not to mention they capture the entire (empirical) distribution which is something that otherwise often gets lost when doing descriptive statistics. Building from that we will delve into related problems of sampling in a stream setting, and updating in a batch setting; and highlight some cool tricks such as capturing time-dynamics via data snapshotting. To finish off we will touch upon algorithms to summarize categorical data, most notably count-min sketch.

Sketch algorithms

Simon Belak

Day 3 ben harrison, met office, arrcc-carissa workshop

ICIMOD

Tendances (13)

Feature Geo Analytics and Big Data Processing: Hybrid Approaches for Earth Sc...

Jiit 2013 14 project presentation aniket mishra

Iot data aggrigation

02 sandia pv modeling conference 9 may2017

KatRisk RAA 2018 Highlights

Big data, Cloud, and the NOAA CRADA at The Climate Corporation

Undergraduate Modeling Workshop - Air Quality Working Group Final Presentatio...

Large Scale Stream Analytics using a Resource-constrained Edge

Meniscus Advanced Energy Analytics Platform

I²: Interactive Real-Time Visualization for Streaming Data with Apache Flink ...

NOAA's Climate Data Record (CDR) Program

Sketch algorithms

Day 3 ben harrison, met office, arrcc-carissa workshop

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