May 17, 2020

Big Data and Predictive Analytics Tools Project Next Month's Weather Risks Today

Big Data
Risk Management
weather prediction
4 min
Daily access to the odds of extreme weather
Click here to view this article in our digital reader Written byJohn Plavan In sectors such as retail and finance, big data analysis has proven transfo...

Click here to view this article in our digital reader

Written by John Plavan

In sectors such as retail and finance, big data analysis has proven transformative – changing how businesses approach everything from product inventory to fleet efficiency to actuarial strategies. Identifying trends and probabilities from patterns within massive volumes of data has led to powerful new techniques for risk reduction and decision making.

In the energy sector, big-data is becoming a game-changer in an unexpected way - by transforming how we forecast the risk of extreme weather events on the horizon. From gas and power futures to grid infrastructure planning, the implications of big-data for the energy sector are huge.

For years, gas and power traders and risk managers have relied on numerical weather-prediction methods from NOAA’s Climate Forecast System and the European Centre for Medium-Range Weather Forecasts (ECMWF) to judge probabilities of weather events for demand forecasting and hedging. While conventional forecasting tools have become significantly more accurate over the years for short and medium-range forecasts, accurate forecasting beyond a seven-day window has remained challenging.

Numerical weather simulation models tend to be volatile in the 5 to 15 day forecast window due to the limits of parameter initialization scope needed in computer simulation modeling. And it is challenging to fully analyze these effects efficiently in an operational setting. For traders to leverage these traditional forecasts to gain a market advantage or to identify mispricing has typically required time-consuming comparisons of historic events to current patterns.

That’s where big-data driven models are coming into play, enabling forecasters to quantify weather risk with greater accuracy over longer periods. Statistical data analysis methods blended with cutting edge new atmospheric science findings can compare numerous weather variable patterns, observationally quantified around the globe, as precursors to events that might happen 30-40 days in the future. For those investing in gas and power or planning a city grid, the ability to see farther down the road, with less effort, is enormously valuable. With big-data driven predictive analytics models, sophisticated algorithms do the heavy lifting and deliver statistical reliability.

The new analytics approach uses more than 60 years of data on global weather pattern relationships in conjunction with current observational data to objectively quantify the risk for future extreme temperature events - with lead times of up to 40 days.

The data is indeed big. Billions of calculations with hundreds of weather patterns compiled over 10,000 days of observations provide a quantitative probabilistic forecast that identifies the relationships between historical weather patterns, current observational data and long-range (subseasonal) extreme temperature events.

For energy producers and traders, this means daily access to the odds of extreme (i.e. market-relevant) heat waves, cold snaps and temperature events that are often missed by other forecasting methods. These additional data points, entirely independent of numerical model simulations and traditional weather forecasting routines, provide traders with a more comprehensive understanding of weather’s impacts on the natural gas and power markets.

Case in point? The winter of 2011-12. By October of 2011, much of the commercial meteorology world predicted that the winter of 2011-12 would be blisteringly cold. But big data analytical models were presenting a different picture, repeatedly indicating elevated “heat” scores in the Eastern U.S ahead of the short range forecasts.

This objective, algorithmic data was one of few indicators that the winter of 2011-12 in the Eastern U.S. featured elevated risk of warmth, not cold at lead times ahead of the market.

For energy analysts and energy brokers who took advantage of this advanced forecast, prior knowledge of the weather trend was money in the bank.

Advanced knowledge of increased risk for extreme temperature events before the market has factored these events into pricing creates an advantaged play opportunity. Traders leveraging big data driven technology are given uniquely derived information with which to identify mispriced markets ahead of the crowd.

The ability to forecast weather event risks beyond two weeks is something meteorologists and market analysts have long sought – and there’s benefit to be had both in profits and public good. By utilizing powerful new predictive analytics tools to project the weather risks, energy traders, producers and utilities can not only gain an edge in the market, but also increase efficiency; avoid unplanned downtime caused by unforeseen adverse weather and reduce resource costs.

John Plavan is the chief executive officer of EarthRisk Technologies, a San Diego based pioneer in research and analytics for projecting extreme weather phenomena and temperature fluctuation risk over long periods of time.

Read More in Energy Digital's March Issue



Share article

Oct 19, 2020

Itronics successfully tests manganese recovery process

Scott Birch
3 min
Nevada firm aims to become the primary manganese producer in the United States
Nevada firm aims to become the primary manganese producer in the United States...

Itronics - a Nevada-based emerging cleantech materials growth company that manufacturers fertilisers and produces silver - has successfully tested two proprietary processes that recover manganese, with one process recovering manganese, potassium and zinc from paste produced by processing non-rechargeable alkaline batteries. The second recovers manganese via the company’s Rock Kleen Technology.

Manganese, one of the four most important industrial metals and widely used by the steel industry, has been designated by the US Federal Government as a "critical mineral." It is a major component of non-rechargeable alkaline batteries, one of the largest battery categories sold globally.

The use of manganese in EV batteries is increasing as EV battery technology is shifting to use of more nickel and manganese in battery formulations. But according to the US Department of Interior, there is no mine production of manganese in the United States. As such, Itronics is using its Rock Kleen Technology to test metal recoverability from mine tailings obtained from a former silver mine in western Nevada that has a high manganese content. 

In a statement, Itronics says that its Rock Kleen process recovers silver, manganese, zinc, copper, lead and nickel. The company says that it has calculated – based on laboratory test results – that if a Rock Kleen tailings process is put into commercial production, the former mine site would become the only primary manganese producer in the United States.

Itronics adds that it has also tested non-rechargeable alkaline battery paste recovered by a large domestic battery recycling company to determine if it could use one of its hydrometallurgical processes to solubilize the manganese, potassium, and zinc contained in the paste. This testing was successful, and Itronics was able to produce material useable in two of its fertilisers, it says.

"We believe that the chemistry of the two recovery processes would lend itself to electrochemical recovery of the manganese, zinc, and other metals. At this time electrochemical recovery has been tested for zinc and copper,” says Dr John Whitney, Itronics president. 

“Itronics has been reviewing procedures for electrochemical recovery of manganese and plans to move this technology forward when it is appropriate to do so and has acquired electro-winning equipment needed to do that.

"Because of the two described proprietary technologies, Itronics is positioned to become a domestic manganese producer on a large scale to satisfy domestic demand. The actual manganese products have not yet been defined, except for use in the Company's GOLD'n GRO Multi-Nutrient Fertilisers. However, the Company believes that it will be able to produce chemical manganese products as well as electrochemical products," he adds.

Itronics’ research and development plant is located in Reno, about 40 miles west of the Tesla giga-factory. Its planned cleantech materials campus, which will be located approximately 40 miles south of the Tesla factory, would be the location where the manganese products would be produced.

Panasonic is operating one of the world's largest EV battery factories at the Tesla location. However, Tesla and other companies have announced that EV battery technology is shifting to use of nickel-manganese batteries. Itronics is positioned and located to become a Nevada-0based supplier of manganese products for battery manufacturing as its manganese recovery technologies are advanced, the company states.

A long-term objective for Itronics is to become a leading producer of high purity metals, including the U.S. critical metals manganese and tin, using the Company's breakthrough hydrometallurgy, pyrometallurgy, and electrochemical technologies. ‘Additionally, Itronics is strategically positioned with its portfolio of "Zero Waste Energy Saving Technologies" to help solve the recently declared emergency need for domestic production of Critical Minerals from materials located at mine sites,’ the statement continues.

The Company's growth forecast centers upon its 10-year business plan designed to integrate its Zero Waste Energy Saving Technologies and to grow annual sales from $2 million in 2019, to $113 million in 2025.

Share article