Envision Energy: Use machine learning technology to achieve an energy efficient and low-carbon future in China
How to release the full potential of renewable energy? Energy internet and machine learning would the right combination to achieve the energy efficient and low carbon future for China.
Envision Energy is one of the biggest renewable energy developer and energy digitalization technology provider in China. The company believes that the energy Internet will be the operating mechanism of the renewable energy era. This mechanism can be applied in changing the uncertainty, intermittency, volatility, and distributivity of renewable energy, making energy go digital and shape it into products and software.
The renewable energy capacity has been growing rapidly and China set ambitious targets for green energy transition. At the start of 2017, China announced that it would invest $360 billion in renewable energy by 2020 and scrap plans to build 85 coal-fired power plants.  China is at the center of a global energy transformation, which is being driven by technological change and the falling cost of renewables. China’s National Development & Reform Commission (NDRC) has written a draft policy that would increase the renewable energy target from 20% to 35% by 2030. 
Riding these trends, the energy companies are faced with several key challenges. First of all, a large amount of energy is wasted every day due to the mismatch of supply and demand. Because the constructions of solar panels and wind farms in China has developed very aggressively while upgrades to its electrical grid have not yet been completed. Moreover, the capacity to analyze energy usage is missing for the end users such as industrial producers, power plants, building operators.  In addition, the lagging in data aggregation for the distributed CHP (Combined Heat & Power) system.
To solve these problems, Envision Energy provided integrated intelligent technologies that work together across business processes, enterprise applications, and platforms. Envision launched its cloud-based EnOS™ platform at Europe in 2016.  EnOS™ is a smart, scalable and open Energy IoT platform which now helps manage over 100GW of energy assets globally. Through real-time computing, machine learning and big data analytics, envision provides digital solutions to monitor and control how, where and when energy is produced, transmitted, stored and consumed.  “EnOS™ is Envision’ s Energy IoT Operating System based on open source and machine learning technology. Currently, the platform helps manage over 100GW energy assets globally, connecting over 50 million sensors and smart devices. The company is using them to create an end-to-end solution that drives better outcomes and boost results. In the founder, Zhanglei’s vision, “the energy system in the future will connect billions of power plants, photovoltaic panels, energy storage battery, charger network, and energy-consuming equipment, forming an actual super brain for the power.”  The first-hand access to those data through hardware is one of the key advantages of Envision. The management of Envision wants to leverage the data to the fullest as there are tons of valuable data generated from the connected energy internet every day. Machine learning techniques are widely applied to provide real-time analytics and increase the accuracy of data prediction so that the energy production by renewable power stations can be optimized. In the future, the responsive system of the grid can be largely improved with machine learning technology, ultimately solving the miss match of supply and demand side and generating less energy waste. The success of the implementation will not only depend on how quickly the technology is evolved but also depend on how fast the data has been acquired.
For what the company should do in the future, I think the individual’s efforts and impact should not be underestimated. The environmental and sustainability community in China has been growing fast and is playing a much more active role nowadays. The company provides energy management system offerings mainly to industrial players, but I would recommend Envision Energy provide products to help track individual energy consumption and optimize the energy consumption. Envision can access individual energy usage data through collaboration with other big data aggregators such as bill payment system and utility companies.
One thing I am not so sure about is that the scalability of energy ecosystem and the effectiveness of machine learning might be hugely hindered by the implementation and acquisition of data. Most important foundation for big data and machine learning is a large amount of “data”. To integrate the “end to end” energy ecosystem and collect enough data enabling machine learning to add real value, the company has to work with various types of customers and stakeholders to implement its data analyzing products. However, the energy sector in China is highly regulated and key players are mostly state-owned companies, which are much less open to new technology and innovation. I can imagine it would be extremely challenging for Envision Energy to build track records and gain trust from these conservative SOE (state-owned enterprises) players, especially when accessing data.
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 Jonathan Woetzel and Jiang Kejun, “China’s renewable energy revolution,” August 2017, https://www.mckinsey.com/mgi/overview/in-the-news/china-renewable-energy-revolution
 Kurt Lowder,“China Proposes 75% Increase To 2030 Renewable Energy Target,” CNBC, Sep 27th, 2018, https://cleantechnica.com/2018/09/27/china-proposes-75-increase-to-2030-renewable-energy-target/
 Research Briefs, “5 Ways The Energy Industry Is Using Artificial Intelligence,” March 2018, https://www.cbinsights.com/research/artificial-intelligence-energy-industry/
 Engerati, “Machine learning, IoT and big data for energy efficiency: a use case,” Jul. 2018, https://www.engerati.com/energy-management/article/energy-efficiency/machine-learning-iot-and-big-data-energy-efficiency-use
 Mike Kavis, “Envision Energy Leverages IOT Technologies To Optimize Renewable Energy,” Feb 2015, https://www.forbes.com/sites/mikekavis/2015/02/13/envision-energy-leverages-iot-technologies-to-optimize-renewable-energy/#27690410745e
 Christoph Steitz, Ludwig Burger, “Envision Energy to spend $1.1 billion on Europe expansion: CEO,” Sep 2016,https://www.reuters.com/article/us-europe-windpower-envision/envision-energy-to-spend-1-1-billion-on-europe-expansion-ceo-idUSKCN11X1MV
 Paul Dvorak, “Envision Energy launches ‘Energy IoT and Smart City Technology Alliance’, July 2017, https://www.windpowerengineering.com/business-news-projects/business-issues/envision-energy-launches-energy-iot-smart-city-technology-alliance/
 Envision Energy, “Envision uses digital expertise to enhance partnerships and provide a platform for digitalization of the wind industry,” Nov 2017, https://www.prnewswire.com/news-releases/envision-uses-digital-expertise-to-enhance-partnerships-and-provide-a-platform-for-digitalization-of-the-wind-industry-300564847.html
 Gavin Mooney, “10 Ways Utility Companies Can Use Artificial Intelligence And Machine Learning,” May 2018, https://www.digitalistmag.com/digital-economy/2018/05/17/10-ways-utility-companies-can-use-artificial-intelligence-machine-learning-06167501
 James McClelland, “Connected Assets: How Machine Learning Will Transform the Utilities Industry,” Feb 2018, https://www.digitalistmag.com/digital-economy/2018/05/17/10-ways-utility-companies-can-use-artificial-intelligence-machine-learning-06167501
Student comments on Envision Energy: Use machine learning technology to achieve an energy efficient and low-carbon future in China
I agree with your skepticism of the scalability of envision’s utility management system. While in an ideal world the founder’s hope for an “energy system in the future [that] will connect billions of power plants, photovoltaic panels, energy storage battery, charger networks, and energy-consuming equipment, forming an actual super brain” would be possible, I believe the computing costs–in terms of actual hardware and physical dollars–for such a system would be astronomical. For this reason, I believe that Envision should focus its near term goal on collecting data from IoT products to better forecast demand.
This is an interesting application of IoT that could help optimize the grid for renewables. The article points out (as do many observers of the energy industry) that battery storage and charging will have to be used in order to smooth out the supply and demand of renewable energy. However, those technologies are often cost-prohibitive and feel several years away from full-scale implementation. I’m curious whether the company sees a path forward without these technologies in place and if their platform can be used as a bridge between today and a future where battery storage is far more accessible.