The role of AI in energy optimization

Energy systems are undergoing a profound transformation. Rapid electrification, expanding renewable generation, and the exponential growth of data-driven industries are placing unprecedented pressure on power systems and industrial energy consumption. Can artificial intelligence (AI) optimize energy use to maximize benefits for people as well as the environment? ABB’s industrial AI solutions, including analytical AI and generative AI (GenAI), are powerful tools to deliver AI energy optimization across industry, buildings, grids, and digital infrastructure.

Web Story

7min

2026-04-13

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Industrial AI improves efficiency by optimizing equipment performance, predictive maintenance, and process control

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Energy optimization reduces both operational costs and carbon emissions

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ABB’s digital platforms are enabling scalable energy intelligence and digital transformation across industries

AI Energy Optimization

According to the World Economic Forum, AI can reduce energy consumption in buildings, factories, and energy systems by improving forecasting, automation, and operational optimization.¹ At the same time, the importance of energy optimization is increasing as global electricity demand rises due to electrification and digitalization, including AI workloads themselves. An AI-centric power plant has the potential to deliver $1 million to $5 million in annual savings per gigawatt of capacity.² Research into physics-informed neural networks, automated control and data infrastructure will accelerate energy optimization and sustainable impact.³ In addition, the role of people in solving challenges around accuracy of AI techniques must not be underestimated. Data cleansing, anomaly removal, analyzing the correlation of parameters and proper result interpretation are as important as ever.⁴

The energy optimization and AI paradox

Energy optimization maximizes benefits for people as well as the environment, and consists of three concurrent strategies: saving energy, managing demand response and using renewable energy sources.⁵ Energy efficiency has long been a strategic priority across industries and there are savings to be made.

Source: Based on IEA Analysis from 2024 [6]

Increasingly volatile energy pricing and supply is intensifying the focus on smart energy consumption and management. McKinsey estimates that generative AI could create $2.6–4.4 trillion in global economic value annually, with significant impact in industrial operations and energy-intensive sectors.⁷ However, this digital transformation also brings increased electricity demand. But while AI consumes energy, it also provides powerful tools to optimize energy use across the entire energy ecosystem.⁸

Megatrends shaping AI-driven energy optimization

Electrification of industry and transport

increasing power demand

Rapid growth of AI and digital infrastructure

including energy-intensive data centers

Decentralized energy systems

integrating renewables, storage, and microgrids

Industrial digitalization and Industry 4.0

where AI-driven analytics optimize operations

Decarbonization targets

pushing companies to reduce energy use and emissions

Energy-as-a-service models

enabling scalable optimization platforms

How do ABB’s AI enabled energy optimization solutions solve energy challenges?

In the last two years ABB has invested in companies such as Ndustrial and GridBeyond, specialising in AI powered energy management solutions. Here are some of ABB's AI-enabled solutions delivering energy optmization for industrial customers.

Energy-Intensive Industrial Processes

Industries such as cement, metals, mining, chemicals, and pulp and paper rely on energy-intensive production processes. Small inefficiencies in process control can translate into significant energy waste.

Energy can represent a substantial portion of operating costs in these industries. Improving process efficiency directly improves profitability while reducing environmental impact.

ABB Ability™ Expert Optimizer

ABB Ability™ Expert Optimizer uses advanced process control and AI-driven optimization algorithms to continuously adjust operational parameters in real time. By stabilizing production processes and optimizing equipment operation, the solution helps reduce energy consumption per unit of output while maintaining product quality.

Fragmented Operational and Energy Data

Operational, asset and energy data are often stored in different systems that do not communicate effectively. Without integrated data analytics, companies cannot identify the root causes of inefficiencies across assets and processes.

ABB Genix™ IIoT and AI Suite

ABB Genix™ Industrial Internet of Things (IIoT) and AI Suite integrates operational technology (OT), IT systems, and engineering data into a unified platform. AI models analyze these data streams to identify performance gaps, predict equipment behavior, and optimize operations across the enterprise.

Rising Energy Costs and Market Volatility

Energy prices are increasingly volatile due to geopolitical factors, evolving energy markets, and fluctuating renewables generation.

Facility managers must manage consumption levels, track real-time energy sources and manage the influence of external factors such as the weather, to control costs, meet targets and report with confidence.

ABB Ability™ Nsight™

ABB Ability™ Nsight™ helps facility managers control and reduce energy costs. Machine learning grid predictions provide suggestions to control load based on real-time market prices and demand-response. Production, finance and budgeting data for each facility are aligned with KPIs and daily weather information to provide a contextualized baseline for actionable energy suggestions that keep the human-in-the-lead.

From energy monitoring to energy intelligence

As efficiency initiatives become more digital and interconnected, closing the gaps in skills, data quality, and internal alignment now matter as much as budget.⁹ By using platforms like ABB Ability™, companies can adopt an AI-driven, integrated approach that links energy systems, processes, and digital intelligence - improving efficiency and lowering costs.

Key case studies around the world

Germany - Schwarzenbruck

Germany - Schwarzenbruck
Germany - Wiesbaden
Norway
Jordan
Sweden

ABB technology at the core of an energy-intelligent wastewater treatment plant The Schwarzenbruck wastewater treatment plant in Germany uses the ABB Ability™ OPTIMAX® energy management system to monitor, control, and optimize complex energy flows. By integrating hydroelectric, photovoltaic, combined heat and power, and multiple storage systems, the plant balances energy production and consumption with minimal reliance on the grid. The system calculates optimal storage usage based on load forecasts, enabling 100 percent reduction in previous grid electricity demand and saving ~300 tons of CO₂ annually. This energy-intelligent, grid-serving operation demonstrates advanced energy optimization.

AI module delivers 1.5 percent reduction in energy costs The ABB Ability™ OPTIMAX® 6.4 AI module upgrade enhances industrial energy optimization by improving accurate forecasting of load demand, energy generation, and pricing, while reducing nomination errors and penalty costs. The system enables coordinated control of multiple assets, improving efficiency and supporting decarbonization across sectors. At InfraServ Wiesbaden’s industrial park power plant, it delivered a 1.5 percent reduction in energy costs within six months, achieving ROI within a year. Its modular design allows easy deployment and automated updates, optimizing complex energy systems and reducing emissions.

AI Energy Optimization for the city of Trondheim Trondheim’s smart city initiative, part of the +CityxChange project, uses ABB Ability™ OPTIMAX® to enable advanced energy optimization across Positive Energy Blocks (PEB). The system leverages a machine learning system for predictive planning to monitor, forecast, and control multi-directional energy flows, integrating photovoltaics, heat pumps, and storage. It also powers Europe’s first peer-to-peer energy trading platform, where 118 million kWh were traded (July 2022–May 2023). By matching supply and demand in real time, OPTIMAX® reduces consumption, supports decentralized energy systems, and minimizes infrastructure expansion, driving a cost-efficient, climate-positive urban model.

ABB’s digital and AI technology is addressing resource scarcity Jordan’s water sector is deploying ABB Genix™ Industrial IoT and AI Suite to improve energy efficiency and resource management across pumping stations, treatment plants, and distribution networks. Selected by the German Agency for International Cooperation (GIZ) and the Water Authority of Jordan, the solution uses real-time, contextualized data and AI to monitor and reduce energy consumption while optimizing operations. In a country with only 61 m³ of renewable water per capita annually and ranked 15th most water-scarce globally, Genix enables data-driven decisions, cost savings, and sustainable water management through advanced digital and energy optimization capabilities.

3 percent reduction in energy consumption Already in 2010 ABB Ability™ Expert Optimizer’s algorithms could improve process stability and energy efficiency in kiln operations at Nordkalk’s Köping limestone plant in Sweden. The system optimized fuel usage, emissions control, and heat recovery while adapting to varying limestone feed types. As a result, the plant achieved over 3 percent reduction in energy consumption and more than 5 percent increase in production rate. The solution also enhanced product quality by stabilizing lime reactivity and supported environmental goals through efficient alternative fuel use and reduced emissions.

Getting real with AI

In conclusion, energy optimization remains a strategic priority as industries electrify, digitalize, and adopt renewable energy. AI energy optimization is increasingly vital to maximizing opportunities and gains across operations, assets, and infrastructure. ABB’s AI-enabled solutions deliver the visibility and analytical insights needed to shift from reactive monitoring to proactive optimization. As energy demand grows, organizations leveraging AI will enhance competitiveness and resilience - ultimately addressing a core industrial challenge: how to deliver more productivity and performance for our customers while using less energy to enable them to outrun.

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