Advances in data collection and interpretation are fueling changes in the global energy sector. With each passing year, new technologies are enabling information to be captured and analyzed at increasingly granular levels. For many, that data is being used to drive cost savings and carbon reduction, with major players in the energy economy making data-driven decisions for optimizing spending and investment habits. One of the most interesting evolving trends in the industry is the relationship between data, artificial intelligence, the evolution of traditional energy practices, and how large-scale impact can be achieved by applying predictive analytics.
Artificial intelligence (AI) has a tremendous role to play in the global energy transition. Where processes are complicated and tedious, AI can accelerate and streamline; where new solutions or models appear risky, AI can increase decision making confidence; where there are gaps in modeling or planning, AI can illuminate likely scenarios. The breadth of applicability throughout the entire energy sector is impressive and ever expanding. Advances in technology, controls, planning, and integration all often leverage artificial intelligence as a way to simplify and expedite processes. AI/ML capabilities have merit for all stakeholders, providing a data advantage to optimize all aspects of project development from any angle of involvement.
Artificial intelligence can have a profound impact on communities, cities, states, and entire nations as they rethink their relationship with power. Creating and implementing a portfolio or region wide energy transformation is no small task, and those with a more conservative approach may consider such a plan too high-risk, opting rather to pilot a distributed energy project or start small and progress piecemeal. However, a comprehensive approach is often the most cost-effective and high value strategy. Tackling a full portfolio or region enables economies of scale for the best available pricing and approaching a strategy with a bird’s eye view means a plan can be put together and implemented with the most information available. An all at once approach is better for the grid – infrastructure sometimes cannot efficiently handle small, one-off projects without a cohesive plan that includes necessary infrastructure upgrades.
On paper, the above portfolio or region-wide approach seems great, but in reality, it is a huge undertaking from a number of standpoints. In order to do this the traditional way, the costs, resourcing, and timelines could be astronomical. This is where AI, machine learning, and other advanced data tools have the power to transform the way we generate, store, distribute, and consume energy. Predictive analytics, remote survey tools, automatic value quantifications, digitized matchmaking and more are all driven in part by AI and can dramatically reshape project development practices.
AI is challenging, improving, and redefining the traditional. We have only scratched the surface of what is possible in terms of the productivity, efficiency, and transformative capabilities of AI in the energy sector. Here at GridMarket, we think a lot about how advances in technology, data, predictive analytics, software, and more, are revolutionizing the energy landscape and changing our collective relationship with power. The fundamental concept behind our proprietary data platform is powered by a wealth of individual data points that creates a narrative around the actionable energy opportunities for a building, a campus, an entire portfolio, an individual land parcel, a regional community, or an entire nation.
The current sector focus on building efficiency, resiliency, and decarbonization puts GridMarket squarely in the center of the national and global energy transition. Our proprietary, AI-driven project optimization platform is the low-risk, high reward solution that the market needs.
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