Articles in “Technology”

How Big Data Makes Renewable Energy More Competitive

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Two weeks ago we attended the Renewables UK Marketplace and I presented the Planet OS story by participating in a session about big data. I was joined by several other leaders from the renewable energy big data market. Paul Usher started off the session and made it clear that renewable energy companies could gain significant competitive advantage through advanced analytics and big data.

However, this business advantage doesn’t come easy because big data analytics are hard to implement, and the most time consuming part is simply preparing the data for analysis. Most big data teams spend 40-60% of their time preparing data or ‘data wrangling’ as it’s called by many. The problem is even worse with geospatial data which has many diverse formats including time series, raster, and vector data. Luckily, there are new emerging technologies like Kinetica and Datahub that help companies reduce the time they spend wrangling geospatial data. The time saved can be spent instead on developing advanced analytical models that deliver competitive advantage to the business.

I observed many parallel themes between this event and the Wind Europe Summit. The wind energy industry is clearly maturing, although in different ways between onshore and offshore. Innovation is a major goal across the entire supply chain as companies rush to increase revenues and drive costs down. New technologies like floating turbines and energy storage could have an enormous impact on the future of renewable energy. Meanwhile the industry is tirelessly optimizing the performance of both operations and assets to reduce costs and be more competitive with traditional energy sources like coal and gas. Advanced analytics is a critical tool for renewable energy companies to achieve this competitive advantage.

Thoughts From The Wind Europe Summit 2016

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As I reflect on what I observed at Wind Europe Summit and Wind Energy Expo in Hamburg last week, I am left with three lingering thoughts.

  • The wind energy industry is clearly maturing as the new projects get larger and less frequent and much more competitive for the operators and suppliers. Also, the market growth rate seems to have slowed some from recent years, and the number of operators and suppliers is beginning to consolidate. All of this adds up to fewer, larger industry players which is a clear signal of a maturing industry.
  • Another obvious trend is the continued innovation and drive to lower the cost of wind energy. There is a relentless theme of cost reduction across all the segments of the value chain, from equipment OEMs, to operations and maintenance, to energy trading and distribution. For example, Sentient Science introduced a new data science model that can predict gearbox component failure. New technology in floating turbines and energy storage has the potential to open up large new markets for wind energy which will increase the market size dramatically to further reduce costs.
  • Finally, many speakers and people I talked to suggested that the industry needs to move beyond component and segment level optimization and start to approach challenges with a full system level view. Hans Bunting, COO of Renewables at Innogy made this point several times during the Technologies for Transition session. To move the industry forward, we need to take a system wide, technology independent, full supply chain view of the problem from the equipment, to the operator, through distribution all the way to the end consumer. Breaking down the barriers across the supply chain and even between different renewable technologies (wind, solar, hydro) is the best way to move the industry forward.

NASA Climate Data Webinar Resources

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On September 21, we hosted our very first Product Webinar with Dr. Sangram Ganguly (NASA Ames & BAERI) and myself, where we discussed How to Easily Analyze NASA Climate Models using the Planet OS OpenNEX Data Tool. I would now like to share some of the resources and materials we used during our session so that you could try them out yourself.

Please let me know at tom@planetos.com if you have questions or comments.

See you at the next #PlanetosWebinar!

Investigate NASA Climate Data Using Planet OS Climate Data Access Tool

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There used to be only two ways to work with climate models: run complex fluid dynamics code on a supercomputer or look at a picture or graph made by someone who did. NASA and Amazon have changed that by publishing the results of model runs from institutions around the world as part of their OpenNEX program. Anyone can now access these datasets for free, meaning that researchers, journalists, students, (quantitative) analysts, risk modelers, etc., can explore the conclusions of top climate change investigators.

Last week, our team at Planet OS announced the OpenNEX Data Access Tool that makes working with this data much easier. With an interactive web interface and transformation tools packaged using state-of-the-art container technology, doing hands-on work with climate model results is now convenient for all who are interested. The OpenNEX data tool is especially valuable for professionals from insurance, agriculture, energy, transportation and many other industries affected by climate.

Next Wednesday, September 21st, Dr. Sangram Ganguly and I will give a webinar where we demonstrate in very practical terms how to select and use this tool to access and use the results of the climate models.

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Planet OS Releases OpenNEX Climate Data Access Tool

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Sept 1, 2016
Palo Alto, California

Planet OS, the world’s leading provider of online geospatial and Earth science data access, today announced the OpenNEX Climate Data Access Tool, which provides an intuitive, web-based interface to important climate datasets provided by NASA Earth Exchange (NEX). The tool requires no programming knowledge, and was developed with the intent to make large-scale climate data accessible by all potential users beyond researchers – risk modelers, software engineers, analysts, city planners, data scientists, and many others.

Due to their immense size, archive structure and file format, incorporating downscaled climate projection data into local or regional analyses can require significant technical effort. The OpenNEX Climate Data Access Tool removes these technical barriers by providing an intuitive web-based interface where users can select and access specific subsets of climate data.

OpenNEX Climate Data Access Tool

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I’m excited to share that today we released our OpenNEX Climate Data Access Tool to help improve access to NASA Earth Exchange (NEX) downscaled climate projections.

Climate information is important not only to highly technical scientists, researchers, and numerical modellers, but also to non-technical local community members and decision makers who are responsible for developing response plans. These downscaled and bias-corrected climate data products provide a better representation of regional climate patterns and are used to determine specific impacts to numerous real-world applications such as crop productivity, flood risk, energy demand, and human health.

However due to their immense size, archive structure and file format, acquiring and incorporating downscaled climate projection data into regional analyses can require significant technical knowledge. The OpenNEX Climate Data Access Tool was designed to remove these technical barriers and allow quick, easy access to these valuable climate data by both scientists and policy planners.

“As climate change is increasingly affecting our everyday life, we are seeing communities requesting easier ways to ingest climate data, both historical and projections, to their work,” says Dr. Sangram Ganguly, Senior Research Scientist at Bay Area Environmental Research Institute and NASA Ames Research Center. “We are excited to collaborate with Planet OS to help communities with the complexities of analyzing space-time bound climate projections datasets. Planet OS’s approach provides a scalable easy-to-use data discovery and integration framework for these big datasets without having to spend days to obtain the data and perform analytics on top of it.”

The OpenNEX Project

In late 2013 NASA Earth Exchange, in partnership with Amazon Web Services (AWS), published a number of earth science datasets within the AWS S3 infrastructure. The goal of the OpenNEX project was to make the data accessible to a wider audience of full-time researchers, students, and citizen scientists.

OpenNEX

Among the datasets published to S3 were two downscaled climate projections: NASA Earth Exchange Downscaled Climate Projections (NEX-DCP30) and NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP). These datasets provide a set of bias-corrected climate change projections and include maximum temperature, minimum temperature, and precipitation for the periods from 1950 through 2005 (Retrospective Run) and from 2006 to 2099 (Prospective Run).

To complement the data, a number of resources were created to assist users in accessing OpenNEX data, including Amazon Machine Images, tutorials, and software development kits. These tools are quite useful for tech savvy users, but can be confusing for those with limited technical knowledge.

Make Better Business Decisions With Advanced Analytics And More Data

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Most data scientist will tell you that with more relevant data their algorithms produce improved results that lead to better business decisions. This is true in many industries, but is especially important in the renewable energy business, where data is a critical tool in becoming more competitive with traditional energy sources like coal and natural gas.

Below are three examples where better business decisions were achieved from improved algorithms and more data:

1. In a recent article on Altenergymag, Vestas Wind Systems used more wind data and advanced analytics to calculate what size turbines to use and where to place them on the Fosen peninsula wind farm in Norway. This reduced the total investment by €150 million, lowered operating and maintenance costs, and changed the project from unfeasible to feasible.

If you are looking for more data to improve your wind energy operation, below is some data available via the Planet OS API:

2. Solar Industry Mag writes about how Locus Energy, Space-Time Insight, and Bidgely used more solar data and advanced analytics to improve solar energy supply forecasting, enable fine grain demand response campaign planning, and enhance real time grid balancing operations. This resulted in better decisions about where to deploy solar generation, faster response to power demand, and lower operating costs for solar power infrastructure.

If you are looking for more data to improve your solar energy operations, below is some data available via the Planet OS API:

  • Downward Solar Flux from NOAA NAM
  • Visible Beam Downward Solar Flux from NOAA GFS

Analyze More, Prepare Less

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It’s a well-documented fact that data analysts spend too much time preparing data rather than analyzing it; estimates range from 40-60% from Blue Hill Research to more than 45% from Ventana Research.

Renewable energy data analysts are likely at the high end of this range given the added complexity of weather data, each source having its own unique format and resolution.

Yet, the renewable energy industry can benefit more than most given the large impact weather data analysis can have on revenue and earnings.  For example, wind farm operators could make millions more per year if they could schedule incremental turbine maintenance just before high wind days. So how can companies get the business benefits without wasting half their data analysts time preparing data?

Try a new cloud service called Datahub that provides thousands of Earth science data parameters through a single API.  You can use it for free at data.planetos.com and see how much more time you have to analyze your data. If you have questions or comments, please let me know at gkleiman@planetos.com.

11 Regional WaveWatch III Forecasts. 6 Months of Unlimited API

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Last week we significantly expanded our wave forecast coverage with 11 regional WaveWatch III datasets, including the North Pacific and North Atlantic Hurricane wave models.

NOAA WaveWatch III Regional Wave Models
The regional WaveWatch III forecasts are updated every six hours and provide 180 hour forecasts (126 hour for Hurricane products) containing wind, wave, and swell data.

The Hurricane wave model uses a blend of GFS winds and hurricane winds at 10 meters above Mean Sea Level (MSL) as forcing conditions, while the Global wave model uses just the GFS forcings and is run side by side with the GFS forcing.

Coverage includes the East and West coasts of the U.S., Alaska, Arctic, North Atlantic, North Pacific, and East Pacific regions, with varying spatial resolution per region.

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GIVEAWAY CAMPAIGN

Our friends at Geoawesomeness are celebrating their 5th birthday with a special Giveaway Campaign. We agreed to participate and will give out six months of unlimited API calls in Planet OS Datahub!

Win 6 Months Of Unlimited API Calls

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Our friends at Geoawesomeness are celebrating their 5th birthday with a special Giveaway Campaign. We agreed to participate and will give out six months of unlimited API calls in Planet OS Datahub!

The award
– 6 months of unlimited API calls (worth over $1000)
– Planet OS T-shirts, for yourself and a friend. We’ll ship them anywhere around the globe.

About Datahub and our APIs
data.planetos.com
– access to 1,200+ parameters of high-quality weather, climate and oceanic data
– growing list of commercial data providers
– easy to use point query RESTful APIs (JSON and CSV)
– extensive API documentation
– interactive iPython / Jypiter notebooks
– active Slack community with online support from our engineers
– launched in March, over 1500 users

How to participate?
- to be eligible to win, share which of the datasets in Datahub is your favorite and why using one of the channels below:
Twitter: tweet your answer to @planetos
Facebook: write your answer to our Facebook wall
Email: email your answer to aljash [at] planetos.com

Sorry, the campaign ended on August 10, 11:59pm PST. Stay tuned for the next ones!

The Winners
- Rakel Logadottir, Logi UAS AS
- Charles Reid, South Seattle College
- Rami Negev, Hewlett Packard Enterprise
Congratulations! We’ll get in touch with you shortly.

Reference
Programmableweb “Planet OS Releases API For World’s Weather And Climate Data”
Geoawesomeness “The First Place To Look For Sensor Data Intelligence”
Geojournalism
GISuser