Utilities and machine learning – the use cases

20180114

Machine learning is finding growing application in the energy sector as analytics and computational capabilities advance, with the potential to meet the changing requirements of an increasingly complex energy system. Machine learning use cases According to a new study from Navigant Research, machine learning - which supports artificial intelligence - is best suited for a handful of specific analytical processes, including clustering, regression and classification. On this basis, some use cases for utilities in which machine learning has advantages over existing analytics techniques, include customer segmentation, pricing forecasting, anomaly detection, fraud detection and predictive maintenance. “The utilities industry is already using self-learning algorithms, particularly in the field of asset monitoring and predictive maintenance, and several reasons suggest the use of machine learning will expand to many more use cases and its adoption will accelerate,” comments Stuart Ravens, Principal Research Analyst with Navigant Research. “During the past decade, it has become easier for companies to deploy machine learning thanks to falling costs, new technological advancements, a softening of conservative attitudes and a fresh approach to analytics procurement.” Utility examples Some applications of machine learning previously covered on Engerati include weather forecasting for renewables dispatch implemented by Vermont Electric Power (Velco) and energy trading. As an example of its capability, Chandu Visweswariah, CEO of Utopus Insights, told Engerati that while the algorithms and forecasting had taken about two years of development, a hyperlocal forecasting capability could now be set up for almost anywhere in the world within a week. Another utility example is Pacific Gas & Electric, which has employed machine learning to increase the accuracy of load reduction forecasts for demand response. Another application for machine learning is cybersecurity. Israeli company CyActive – subsequently acquired by PayPal – exploited the technique to generate and detect malware variants. An example of anomaly detections comes from San Diego Gas & Electric, which has used machine learning to detect power system issues hidden in large data sets. Machine learning for Internet of Things Statistics on machine learning in utilities are limited but according to a 2016 study by zpryme and analytics provider SAS, almost one-third of utilities in North America were then using machine learning for metering and meter data management. Machine learning and artificial intelligence are also fundamental to the Internet of Things. According to Capgemini’s 2017 World Energy Markets Observatory, out of almost 2,900 companies participating in North America’s ‘IoT revolution’, 401 are in the artificial intelligence and machine learning category. And out of the region’s over $73bn investment in IoT, over $12bn is in machine learning and artificial intelligence. Artificial intelligence is envisaged as providing benefits on both the grid and customer sides of the business – think, for example, of the growing use of digital assistants for customer engagement. But the use cases highlighted above indicate, the benefits of machine learning are primarily grid-oriented. Nevertheless, the zpryme survey, utility executives named better customer service as one of the top three benefits envisaged from machine learning, along with increased cybersecurity and improved data driven decision making. The zpryme study offers several recommendations for machine learning adoption. First, these need to be part of wider strategy so that they can be leveraged across the organisation. Their implementation will require change, and learnings can be gained from connections with both other industries and other utilities.

Ист.: Engerati, Ссылка: https://www.engerati.com/energ...
Темы:Энергетика, Будущее,

Предыдущие:

 20180114 A strong European industrial base backing renewables is essential to support the clean energy transition

Ист.:

The first high-level meeting of the renewables section of the EU Clean Energy Industrial Competitiveness and Innovation Forum took place in Brussels on 9th January at the European Commission. Opened by Miguel Arias Cañete, European Commissioner for Energy and Climate Action and chaired by Dominique Ristori, Director-General for Energy, it was attended by a ...
Мета: Miguel Arias Canete, Energy and Climate Action, Dominique Ristori, European Investment Bank, International Renewable Energy Agency, Clean Energy for All Europeans, Juncker, Темы: Энергетика, Транснацики,


 20180114 Improved financial inclusion could boost global bank revenues by US$200b

Ист.:

Banks could generate incremental global annual revenue of US$200b – equivalent to 20% of emerging market banks’ 2016 revenues – by better serving financially excluded individuals and small businesses in 60 emerging countries, according to the EY report Innovation in financial inclusion: revenue growth through innovative inclusion. Driving greater fi...
Мета: India, Jan Bellens, financially inclusive, Темы: Социум, Транснацики,


 20180114 Why challenges lie ahead for the unbreakable bond between hydropower and water

Ист.:

Hydropower is the biggest source of renewable electricity in the world and is responsible for producing roughly 17 percent of the planet"s electricity, according to the International Energy Agency. The scale of some hydropower projects, such as the Itaipu facility situated between Brazil and Paraguay, is vast. Since operations began in 1984, the site has ...
Мета: International Energy Agency, Hydropower, Juliano Portela, Itaipu Binacional, International Water Association, Темы: Энергетика,


По этой же теме:


 20171010 China: dena certifies six new energy-efficient buildings

Ист.:

Pilot projects for energy efficiency in nurseries, office buildings and residential buildings / 70 per cent lower energy consumption than in comparable new buildings The German Energy Agency (dena) has certified six new building projects in China with the highest energy efficiency class A. The buildings consume around 70 per cent less energy than the aver...
Мета: German Energy Agency, China, Center of Science and Technology of Construction, Темы: Энергетика,


 20171103 Every bit counts – Toward an electrified planet

Ист.:

Every bit counts – Toward an electrified planet Since the 2015 G7 summit in Elmau in southern Germany, the world has focused on a common goal: to turn away from fossil energy in favor of electricity as a universal energy source, with the ambition of achieving complete decarbonization by the year 2100. This presents all economies with tremendous chall...
Мета: G7 summit, 3D Model of Energy, Sustainable Energy for All, TenneT, Siemens, Темы: Энергетика, Социум, Будущее,


Следующие:


 20171010 China: dena certifies six new energy-efficient buildings

Ист.:

Pilot projects for energy efficiency in nurseries, office buildings and residential buildings / 70 per cent lower energy consumption than in comparable new buildings The German Energy Agency (dena) has certified six new building projects in China with the highest energy efficiency class A. The buildings consume around 70 per cent less energy than the aver...
Мета: German Energy Agency, China, Center of Science and Technology of Construction, Темы: Энергетика,


 20171103 Every bit counts – Toward an electrified planet

Ист.:

Every bit counts – Toward an electrified planet Since the 2015 G7 summit in Elmau in southern Germany, the world has focused on a common goal: to turn away from fossil energy in favor of electricity as a universal energy source, with the ambition of achieving complete decarbonization by the year 2100. This presents all economies with tremendous chall...
Мета: G7 summit, 3D Model of Energy, Sustainable Energy for All, TenneT, Siemens, Темы: Энергетика, Социум, Будущее,