North America Creating Highest Demand for AI in Energy Management

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AI in Energy Management Market Revenue Estimation and Growth Forecast Report

The use of energy management systems has risen significantly in recent years. This can be attributed to the surging demand for energy, rising energy crisis, declining rate of fossil fuels, and increasing pollution levels due to excessive use of traditional energy resources. With the rapidly increasing population, the need for energy has been growing as well, and as conventional sources cannot fulfill this demand, the focus has shifted to renewable energy sources. Owing to the variable output of such sources, energy management systems are utilized for managing supply and demand.

These days, advanced technologies, such as AI, are being integrated in these systems for improved outcomes. Ascribed to this, the global AI in energy management market is projected to generate a revenue of $12,200.9 million by 2024, increasing from $4,439.1 million in 2018, advancing at a 19.8% CAGR during the forecast period (2019–2024). The adoption of AI has also been increasing for improving the stability of the grid.

A grid system maintains and stores the flow of energy. Energy is stored from a number of sources in the grid system, such as wind power stations, solar power plants, and electricity generation plants. Owing to this, operating grid systems becomes a complex process, which is why, AI is now being utilized for effective management of grid systems. By integrating AI for analysis of massive sets, grid systems can become highly stable and energy efficient, when it comes to managing multiple sources simultaneously.

The key applications of AI in energy management are energy output forecasting, energy generation, energy transmission, and energy distribution. The need for AI has been the highest for energy output forecasting in the past. AI solutions aid utilities in offering efficient energy management services to consumers by using machine learning techniques, data, and statistical algorithms. Energy output forecasting further includes live metering, load forecasting, predictive maintenance, and yield optimization. Other than this, the demand for AI is also projected to rise for energy generation in the coming years.

Education, manufacturing, healthcare, retail, government, residential, and utility are the major end users of AI in energy management. The utility sector made the most use of AI, owing to the fact that advanced systems, including smart meters, are needed by utilities, as they aid in improving the integration of renewable energy by offering real-time information regarding usage statistics and transmission line capacity. This further allows operators to predict energy needs and integrate energy produced from renewable sources into the grid for efficient supply of energy.

Geographically, North America made the most use of AI in energy management in the past, with U.S. creating the highest demand. The country is widely using AI in its energy sector for integrating energy produced from non-renewable and renewable sources into the total energy supply. The Asia-Pacific region is also projected to emerge as the fastest-growing AI in energy management market in the coming years, owing to government initiatives for increasing adoption of AI.

Hence, the market is growing due to the need for effective energy management and use of AI for improving grid stability.

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