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Introduction

Industrial automation has always encouraged the development of technology. Further, every evolution in technology has created great impacts on productivity with huge output. Artificial intelligence changes industries today across the globe, presenting unparalleled insight and automation potential by giving data-driven insights in newer and intelligent ways.

Industries consume huge amounts of energy in all forms, and electricity is one of the most consumed sources, making up more than 42% of the total global demand for electricity as of 2022.

This has huge costs and an environmental impact. Optimizing power usage is not just about saving costs; it is also a crucial step toward minimizing environmental impact and lowering the carbon footprint.

AI in Energy Management

AI implements a data-driven approach toward energy management, thereby enabling industries to:

Monitor usage in real time

The AI systems analyze data from smart meters, IoT sensors, and other devices to show real-time insight into energy consumption, thus enabling them to identify areas of inefficiency and to take immediate corrective actions.

Integration of Renewable Energy

Industries are increasingly integrating renewable energy sources into their energy mix, like solar and wind. Since these sources are variable in output and availability, AI can optimize the use of these variable energy sources by predicting availability and aligning it with production needs.

Smarter Energy Storage Management

AI will contribute to the optimization of charge/discharge cycles of energy storage systems for maximum efficiency and durability in industries dependent on energy storage systems. This is important, especially for companies relying on batteries to store renewable energy.

Renewable Energy Source and Grid Power Usage

AI dynamically balances the consumption of energy between solar power and grid electricity. By analyzing factors like the generation forecast of solar energy, grid power rates, and real-time consumption patterns, AI optimizes energy usage cost-effectively and efficiently. This capability minimizes expensive grid power usage during peak hours and maximizes the use of available renewable energy sources.

Despite its potential, the adoption of AI for power optimization faces challenges such as:

High Initial Investment Costs: The integration of systems with AI is prohibitively costly, especially for SMEs.

Data Silos: Data is another challenge, with data in most cases fragmented and stored in an isolated manner which makes building full analytics impossible. Lack of historical data is a common problem; this limits the possibility of building ML models.

Skill Gaps: Skilled professionals are badly needed to manage and operate the AI-driven systems.

Conclusion

At Akkomplish, we are committed to bringing the best solutions to our customers and partners, and in this line, we are embarking on the journey to bring Renewable Energy Integration using AI into a real-world business solution in 2025.

Our solutions will enable industries to adopt renewable sources of energy and use AI to balance the power from such sources and the grid for seamless operations, thus saving costs and minimizing environmental impact.

For any queries related to energy management, reach out to us now!!

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