{"id":29283,"date":"2025-05-01T09:15:00","date_gmt":"2025-05-01T08:15:00","guid":{"rendered":"https:\/\/www.engineernewsnetwork.com\/blog\/?p=29283"},"modified":"2025-04-25T10:50:31","modified_gmt":"2025-04-25T09:50:31","slug":"predictive-maintenance-can-cut-energy-waste-by-29","status":"publish","type":"post","link":"https:\/\/www.engineernewsnetwork.com\/blog\/predictive-maintenance-can-cut-energy-waste-by-29\/","title":{"rendered":"Predictive maintenance can cut energy waste by 29%"},"content":{"rendered":"\n

Despite 95% of users reporting ROI, predictive tools such as fault detection and diagnostics (FDD) software, AI-powered HVAC analytics, and smart sensors for equipment monitoring remain underused in commercial buildings that account for 26% of global CO<\/strong>2<\/strong><\/sub>\u00a0emissions<\/strong><\/p>\n\n\n\n

The global predictive maintenance market is experiencing unparalleled growth. It is fuelled by advancements in\u00a0Industry 4.0, IoT analytics, and cloud-based systems. Forecasts project a rise\u00a0from $10.6 billion in 2024<\/strong><\/u><\/a>\u00a0to $47.8 billion by 2029, with a CAGR of about 35%.<\/p>\n\n\n\n

According to experts at\u00a0Exergio<\/strong><\/u><\/a>, a company that develops AI-based tools for energy optimization in commercial buildings, predictive maintenance could become an important tool in the global effort to reach net-zero emissions. That especially applies to commercial buildings, which account for 26% of global CO2<\/sub>\u00a0emissions, according to the\u00a0International Energy Agency<\/strong>.<\/u><\/a><\/p>\n\n\n\n

Predictive maintenance uses real-time data and smart algorithms to spot signs of equipment wear or failure before anything breaks. Instead of waiting for a system to malfunction, sensors and software monitor things like temperature, vibration, or energy use to detect unusual patterns.<\/p>\n\n\n\n

“Predictive maintenance has a wide range of applications, from manufacturing to transport,” said Donatas Kar\u010diauskas, CEO of Exergio. “Take buildings, for example. They’re among the world’s largest greenhouse gas emitters. By using AI-driven predictive maintenance tools, it’s possible to significantly reduce energy waste and improve overall efficiency. That can bring us a step closer to the climate goals of 2050.”<\/p>\n\n\n\n

The shift toward AI and machine learning in this space is no longer theoretical. According to a 2025 Honeywell survey, 84% of commercial building managers in the US plan to increase their use of AI within the next year, and 76% expect AI to enhance occupant comfort and productivity. 45% of surveyed buildings already use AI for some operational tasks.<\/p>\n\n\n\n

“In Europe, adoption is progressing more cautiously, but the conversation there is more about how buildings feel and function for the people inside,” continued Kar\u010diauskas. “The predictive maintenance technologies are now linked to real outcomes like air quality, lighting comfort, and even thermal satisfaction. When such systems are in place, occupant satisfaction rises sharply.”<\/p>\n\n\n\n

The expansion of the market indicates that companies see predictive maintenance as a solution to reduce unplanned downtime and avoid expensive emergency repairs. In various sectors, businesses are adopting AI-powered maintenance strategies that help detect system issues early. <\/p>\n\n\n\n

Compared to reactive repair methods, predictive maintenance can cut maintenance costs\u00a0by up to 40%<\/strong><\/u><\/a>\u00a0and reduce equipment downtime by 50%. According to IoT Analytics, 95% of companies using these tools have reported a positive ROI. More than a quarter saw returns in under a year.<\/p>\n\n\n\n

One example of how AI systems connect all the dots can be seen in smart lighting. Modern predictive control algorithms can forecast occupancy and lighting needs in real-time based on previously learned patterns.<\/p>\n\n\n\n

These systems adjust both brightness and color temperature depending on detected activity. It can be a quiet meeting or a high-energy workspace. In some setups, existing CCTV cameras feed into machine learning models to trigger lighting scenarios that support specific tasks without wasting energy.<\/p>\n\n\n\n

Both major players and start-ups are pushing predictive maintenance forward. In Lithuania’s Simbiocity, a living lab of commercial buildings, Exergio integrated its analytics platform alongside an existing BACS system.<\/p>\n\n\n\n

By analyzing over 10,000 signals from building equipment, the system enabled automated adjustments and real-time fault detection. As a result, cooling and ventilation energy use was cut by 44% without compromising occupant comfort.<\/p>\n\n\n\n

“We’re reaching a tipping point where predictive maintenance will no longer be optional, but an expected tool in buildings’ energy management. As more buildings are retrofitted with connected systems, the ability to detect inefficiencies in real time becomes central to decarbonisation strategies.\u00a0We will be able to solve two issues at once\u2013we will save money, and smart technology integrated into infrastructure will support climate goals,” concluded Kar\u010diauskas.<\/p>\n","protected":false},"excerpt":{"rendered":"

Despite 95% of users reporting ROI, predictive tools such as fault detection and diagnostics (FDD) software, AI-powered HVAC analytics, and smart sensors for equipment monitoring remain underused in commercial buildings that account for 26% of global CO2\u00a0emissions The global predictive maintenance market is experiencing unparalleled growth. 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