January 30, 2020

How Artificial Intelligence can enhance renewable energy development

In the next few years, artificial intelligence is anticipated to have a significant impact on enhancing the efficiency of the clean energy industry by automating a number of operational procedures within the solar and wind markets.

Artificial Intelligence (AI), combined with a number of innovative technologies like Machine Learning (ML) and Deep Learning has already clearly demonstrated their potential to enhance the energy and utility market. With growing pressures to reduce carbon emissions and an increase in decentralised systems, energy and utility businesses are investing in AI to manage the changes in demand and supply levels created by an increasing share of renewable energy.

A number of studies have been released exploring the existing and future applications of AI within renewable energy, demand and infrastructure management. Combining the technology with other innovative tools like Big Data, Cloud and the Internet of Things (IoT), AI can actively support electricity grids, enhancing the accessibility of renewable energy systems.

In the next few years, AI is anticipated to improve the efficiency of the renewable energy market by automating operations, particularly within the solar and wind sectors. The technology will also enable utility providers to create new business and service offerings. Aside from creating a more efficient, flexible and intelligent electricity system, AI services can support energy and utility businesses gain a deeper understanding of customer behaviours within various sectors. Sophisticated information from machine learning systems, along with real-time weather data and climate models can be used to generate accurate forecasting models related to the electricity created from specific systems. 

  • AI applications can deliver a range of opportunities for the energy and utility market including the following:-
  • Expanding software solutions capable of measuring large volumes of data, identifying trends, detecting errors and making more accurate predictions.
  • Supporting the development of smart-based services that can make automated accurate decisions designed on learning. This promotes the incorporation of AI with a wider range of applications.
  • Creating customer-focused solutions that understand customer requirements and make automated recommendations.
  • Applying predictive analytics to enhance equipment operations and maintenance and predict potential downtime which can extend product lifetime.