In an increasingly competitive world with scarce resources and a deteriorating climate, Artificial Intelligence can help organizations navigate their path to sustainability and efficiency. AI has the potential to lower energy costs, cut energy waste, and accelerate the use of clean, renewable energy sources in power grids.
Industry success depends on achieving a balance between supply generation, efficiency, and sustainability of energy. Advanced economies across the globe are already harnessing the power of AI. For example, in the UK, Deep Mind has applied machine learning algorithms to 700 megawatts of wind power in the central US to predict power output 36 hours ahead of actual generation using neural networks trained on weather forecasts and historical wind turbine data. Meanwhile, India has an installed capacity of 75GW from various renewable energy sources (wind, solar, etc.) and has a target of 175GW from renewable sources by 2022. These are only a few of the many tangible ways in which AI is revolutionizing the Energy Sector.
Despite these massive leaps in technology, the Energy Sector needs to address some enormous challenges. Some of which are listed below:
Rising CAPEX/OPEX costs for aging infrastructure, grid expansion, and system integration for renewables.
Integration of Distributed Energy Resources
Cost optimization in the transformation process towards green energy and integrating decentralized renewables and their fluctuating electricity output into SMART grid infrastructure.
Challenge of ensuring a consistent, company-wide execution across large and increasingly complex supply chains as well as subsidiaries. Shifting sustainability focus from strategy setting to successful rollout.
Meeting Stakeholder Expectations
Need for managing large amounts of data from multiple assets and increased integration of decentralized renewables.
Collaboration and management of distributed assets
A dynamic, complex, and data-driven environment driven by different digitization processes requires collaborative participation from multiple contributors.
In response to these challenges, progressive organizations need to strategize to transform sustainability parameters for future generations. Here are some ways businesses can bridge the gap between the present and the future of the Energy Sector:
- Create a complete framework to shape and report goals, strategies, and activities to implement best practices to reduce GHG emissions and build climate resilience.
- Use a common language and shared purpose of helping connect the organization’s business strategies.
- Strengthen stakeholder relations and keep pace with Govt. policy developments directed towards meeting climate change obligations under the Paris Agreement.
- Generate economic value; Deliver Return on Sustainable Strategy & effectiveness using AI, ML, and design-led solutions.
- Identify future business opportunities as a part of a sustainable development approach.
- Enhance the value of corporate sustainability by maximizing 4.0 principles, including connectivity and continuous improvement to set energy efficiency improvement goals.
With skillful application, AI can improve the reliability of existing power grids by turning them into smart grids through the use of data to make predictions and decisions. Machine Learning and Neural Networks play an essential role in improving forecasts in the energy industry.
For a successful, AI-led transformation, the Energy Sector has to set some ground rules regarding each stakeholder’s roles and responsibilities in the value chain. There’s a need for regulations around privacy, cybersecurity, and data accessibility in addition to an accurate assessment of the technical requirements, including hardware, software, and human expertise. With the right strategy in place, Industry 4.0 can usher in a bright future for the Energy Sector.