Digitalization – Go Energy Save

Digitalization” is the goal of industrial upgrading, and the existing “Digitalization” is to display data from different specialties, facilitate the operation and maintenance management of the industry, and provide a basis for human decision-making. And “digitalization” is based on “digitization”, combining professional knowledge, AI algorithms, etc. to make optimal choices for humans in certain fields. In practical applications, digitalization has liberated human resources and improved energy application efficiency, which is the direction for future investment and development of humanity. Against the backdrop of vigorously promoting the “dual carbon” policy in our country, “digitalization” is indispensable and can achieve “energy conservation”, “carbon saving”, and “money saving”. In order to cope with global climate change, China has put forward the goals of “carbon peak” and “carbon neutrality”. In September 2020, it was explicitly proposed to achieve “carbon peak” by 2030 and “carbon neutrality” by 2060.

Energy consumption in buildings accounts for approximately 40% of global energy use, making it a critical area for sustainability efforts. Residential buildings alone contribute about 20-25% of this total, driven by heating, cooling, lighting, and appliance use, while commercial structures like offices and retail spaces make up the remaining 15-20%, with higher demands from ventilation systems and round-the-clock operations. In regions with extreme climates, HVAC systems can account for up to 50% of a building’s energy draw, exacerbating consumption in poorly insulated or outdated structures. Efforts to reduce this footprint have led to a rise in energy-efficient retrofits, with modern buildings incorporating smart meters and insulation that can cut usage by as much as 30%. Despite these advances, the rapid pace of urbanization—projected to increase building energy demand by 50% by 2050—underscores the need for widespread adoption of net-zero designs.
Companies, particularly in energy-intensive sectors, are responsible for roughly 30-35% of global energy consumption, with manufacturing and industrial processes leading the charge at around 25% of the total. For instance, steel and chemical production can see energy costs comprise up to 40% of operational expenses, while data centers—powering the digital economy—now consume about 1-2% of global electricity, a figure doubling every four years due to AI and cloud computing growth. Corporate adoption of renewables has surged, with some tech giants sourcing 100% of their energy from solar or wind through power purchase agreements, yet fossil fuels still dominate industrial energy at over 70%. Small and medium enterprises, which account for 13% of global energy use, often struggle to match these strides, as upgrading to efficient machinery or onsite generation might reduce consumption by 20-30% but requires significant upfront investment. As energy costs and climate regulations tighten, companies face mounting pressure to optimize their 30-50% share of regional electricity grids.

One compelling case of energy saving comes from the retrofitting of the Empire State Building in New York City, completed in 2011 as part of a $550 million renovation. By upgrading windows, adding reflective insulation, installing energy-efficient lighting, and optimizing HVAC systems, the iconic skyscraper reduced its energy consumption by approximately 38%. This translated to annual savings of $4.4 million in energy costs and a cut of 105,000 metric tons of CO2 emissions over 15 years. The project demonstrated that even older buildings—originally constructed in 1931—could achieve significant efficiency gains through targeted upgrades, inspiring similar efforts in commercial properties worldwide. The success hinged on a combination of technology and tenant engagement, proving that behavioral shifts, like adjusting thermostat settings, can amplify structural improvements.

Evaluating HVAC system performance is essential for ensuring energy efficiency, cost-effectiveness, and occupant comfort. Key metrics such as Coefficient of Performance (COP), Energy Efficiency Ratio (EER), and Seasonal Energy Efficiency Ratio (SEER) are used to assess equipment efficiency, while baseline comparisons and benchmarking against industry standards like ASHRAE 90.1 or the IECC help measure improvements over time. Tools such as building management systems (BMS), smart meters, and variable frequency drives (VFDs) provide real-time data, enabling precise monitoring of energy usage and system responsiveness. Formal Measurement and Verification (M&V) protocols, such as those outlined in the International Performance Measurement and Verification Protocol (IPMVP), further ensure that actual savings match projected performance outcomes.

Load forecasting plays a pivotal role in energy savings by enabling utilities and building managers to predict electricity demand with precision, thus optimizing supply and reducing waste. For instance, in commercial buildings, advanced load forecasting models—using historical data, weather patterns, and occupancy trends—can anticipate peak usage periods, such as hot summer afternoons when air conditioning demand spikes. By adjusting HVAC schedules or shifting non-essential tasks to off-peak hours, facilities can cut energy consumption by 10-15%, as demonstrated in smart building projects like those in Singapore’s business district. This proactive approach minimizes reliance on expensive, carbon-intensive backup generators, which often kick in during unanticipated surges, and helps balance grid loads, saving both costs and emissions.
In industrial settings, companies like Tesla have harnessed load forecasting to streamline energy use across their manufacturing plants, such as the Gigafactory in Nevada. By integrating real-time data from production schedules, equipment performance, and renewable energy availability (e.g., onsite solar), Tesla forecasts energy needs with an accuracy that reduces excess consumption by up to 20%. This allows the company to store surplus solar power in battery systems during low-demand periods and deploy it during peaks, avoiding grid overdraws that can account for 30% of a factory’s energy bill. Such strategies not only lower operational expenses but also enhance resilience against power price volatility, proving that load forecasting is a scalable tool for energy savings across high-stakes industrial operations.

Algorithmic models designed to save energy have become transformative tools, particularly in optimizing building management systems (BMS). For example, machine learning algorithms, such as those deployed in Google’s DeepMind project, have been used to enhance energy efficiency in data centers by predicting cooling needs with remarkable accuracy. By analyzing variables like server load, external temperatures, and historical usage patterns, the model reduced cooling energy consumption by 40%, slashing overall power use in facilities that account for 1-2% of global electricity. This approach relies on continuous learning, where the algorithm refines its predictions over time, enabling preemptive adjustments to HVAC systems or lighting that can save buildings up to 25% on energy bills. The success has spurred adoption in commercial real estate, where similar models now dynamically balance comfort and efficiency.

Efficient cold storage systems are critical for energy savings in industries like food processing, pharmaceuticals, and logistics, where refrigeration can account for up to 60-70% of total energy consumption. A standout example is the implementation of variable speed compressors and advanced insulation in cold storage warehouses, such as those operated by Lineage Logistics. By adjusting compressor speeds to match real-time cooling demands—rather than running at full capacity constantly—these systems have reduced energy use by 20-30%, saving millions of kilowatt-hours annually across large-scale facilities. Enhanced insulation, like vacuum-insulated panels, further minimizes heat infiltration, cutting the energy needed to maintain sub-zero temperatures by an additional 10-15%. These upgrades not only lower operational costs but also align with stricter environmental regulations on refrigerant emissions.
Another innovative approach to efficient cold storage comes from thermal energy storage (TES) systems, which have been adopted by companies like Viking Cold Solutions in supermarket chains and distribution centers. TES technology uses phase-change materials to store cold energy during off-peak hours, when electricity rates are lower, and releases it during peak demand, reducing reliance on grid power by up to 35%. In a typical 50,000-square-foot facility, this can translate to a 25% drop in refrigeration energy costs while stabilizing internal temperatures more effectively than traditional systems. Paired with smart controls that optimize defrost cycles and monitor door openings, these solutions prevent energy spikes—often responsible for 10-20% of losses in conventional setups—demonstrating how integrated design can make cold storage both cost-effective and sustainable.

Efficient heating systems are essential for reducing energy consumption in buildings, where heating can account for 40-50% of total energy use, particularly in colder climates. A prime example is the widespread adoption of heat pumps, such as those installed in Scandinavian homes and commercial spaces. Unlike traditional electric or gas heaters, heat pumps transfer heat from the air or ground, achieving efficiencies of 300-400%—meaning they deliver three to four units of heat for every unit of electricity consumed. This can cut energy use by up to 50% compared to conventional systems, as seen in Norway, where heat pumps now heat over 60% of households. Paired with smart thermostats that adjust temperatures based on occupancy, these systems minimize waste, offering annual savings of hundreds of dollars per household while shrinking carbon footprints.

The integration of energy-efficient pumps in heating, ventilation, air conditioning (HVAC), and water systems significantly reduces energy consumption while maintaining optimal performance. Modern pumps, equipped with variable frequency drives (VFDs), adjust motor speed to match real-time demand, avoiding the energy waste associated with constant-speed operation. For instance, in a commercial building, a VFD-controlled pump can reduce energy use by up to 50% during periods of low demand, such as off-peak hours. Additionally, advanced pump designs with high-efficiency motors and optimized hydraulics further minimize power losses. By reducing electricity usage, these pumps not only lower operational costs but also contribute to a reduced carbon footprint, aligning with global sustainability goals.
Similarly, energy-efficient chillers play a critical role in minimizing energy consumption in cooling systems, which are often significant energy hogs in industrial and commercial settings. Modern chillers employ technologies like magnetic bearing compressors and advanced heat exchanger designs to achieve higher coefficients of performance (COP), meaning they deliver more cooling output per unit of energy input. For example, a chiller with a high COP can reduce energy usage by 20-30% compared to older models, especially when paired with smart controls that optimize operation based on ambient conditions and load requirements. By integrating chillers with energy management systems, facilities can further enhance savings through predictive maintenance and load shifting, ensuring both cost efficiency and environmental benefits over the system’s lifespan.