Artificial Intelligence (AI), along with a slew of advanced technologies such as machine learning, deep learning, and advanced neural networks, has demonstrated huge potential to transform the energy and utilities sector. With decarbonization, decentralization, and the rollout of novel technologies, utilities, independent power producers (IPPs), and other energy companies are employing AI to manage the imbalance in demand and supply caused by the growing share of renewable energy sources.
Frost & Sullivan's recent analysis, Impact of Artificial Intelligence (AI) on Energy and Utilities, 2018, includes a detailed analysis of current and future applications of AI across the segments of renewables management, demand management, and infrastructure management. It examines technology, geographical, as well as industry trends, key companies that dominate the global AI market, and presents use cases with specific applications of AI.
"In combination with other technologies like Big Data, cloud, and Internet of Things (IoT), AI can support the active management of electricity grids by improving the accessibility of renewable energy sources," said Swagath Navin Manohar, Research Analyst, Energy & Environment.
Over the next several years, AI is expected to boost efficiencies across the renewable energy sector by automating operations in the solar and wind industries. It will also allow utilities and IPPs to launch new business and service models.
"In addition to making the electricity system intelligent and flexible, AI algorithms help utilities and energy companies understand and optimize consumer behavior and manage energy consumption across different sectors," noted Manohar. "Meanwhile, complex machine learning algorithms combined with real-time weather data from satellites, ground-based observation, and climate models can be used to forecast the electricity generated by RES like wind, solar, and ocean."
AI-based applications can create further revenue opportunities for the energy and utility sector by:
- Empowering software applications to analyze large data sets, identifying patterns, detecting anomalies, and making precise predictions.
- Aiding the development of smart applications that can autonomously make accurate decisions based on learning. This drives AI's integration with a wide range of applications.
- Enabling customer-centric solutions that understand evolving customer needs and make automatic recommendations.
- Using predictive analytics to improve equipment O&M and predict downtime, which can extend the lifetime of the equipment.
- Facilitating active customer participation in demand-response programs using game theory algorithms and leveraging blockchain to protect data.