There are a growing number of technologies that are helping to decarbonize the state of Minnesota and the world. Solar, wind, heat pumps, geothermal systems and more are all moving the state forward on its path to a clean energy future. One piece of the puzzle that is monumentally useful in helping reduce energy use, decrease emissions, and increase efficiencies is data. Knowledge is power and the more we know about the systems we have in place, the better we can plan for their future. We spoke with two of CEEM’s member-experts in this field, EnergyPrint and NextEra Analytics, about the huge role data can play in Minnesota’s transition to a clean energy economy.

For this topic, we spoke with two experts in the field: Stephanie Judge, Head of Energy SaaS of NextEra Analytics and Wade Smith, CEO of EnergyPrint.

NextEra Analytics is a subsidiary of NextEra Energy Resources, which is the world’s largest generator of energy from the wind and sun and a world leader in battery storage. Their comprehensive energy management software, NextEra 360™, is used to turn energy data into action. This means they are helping customers to

• Track and aggregate carbon emissions data
• Design and execute cost effective energy systems; and
• Increase operational efficiency and reduce costs of existing assets – both those that produce and consume power

EnergyPrint helps its clients reduce their energy footprint an average of 12.7%, by analyzing their utility use, benchmarking against 6,800 similar buildings in its database, helping set goals and working with local contractors to install efficiency improvements. EnergyPrint then measures results and verifies those savings at the utility meter. The simple payback on an annual subscription to EnergyPrint’s software is 10 days with an average savings of $36,000 a year!

Data plays a pivotal role in the clean energy industry by providing the necessary insights and tools to drive improvements and achieve decarbonization goals. Without access to performance data, analytics, and diagnostic information, energy usage in buildings tends to drift higher, making it challenging to identify areas for improvement. Similarly, just as every business generates financial statements and budgets to track their financial performance, energy use also requires goals, performance metrics, and a common basis of comparison to set improvement targets and measure progress.

In the clean energy industry, data serves as a critical resource to support the decarbonization of the economy and the built environment. Through the utilization of both software and hardware interventions, data-driven strategies can be employed to reduce emissions and enhance energy efficiency. For instance, in the commercial and industrial sector, businesses leverage software solutions to mitigate emissions in their existing operations while working on long-term plans to replace their hardware as it makes sense in their business plan.

A concrete example of data-driven interventions is seen in the collaboration between NextEra Analytics and a manufacturing facility in Nebraska. By utilizing its algorithmic dispatch strategies, NextEra Analytics assisted the facility in reducing net load during peak hours. This approach not only provides cost savings but also lowers emissions. Depending on the specific conditions and the power market, models indicate potential emissions reductions in excess of 20% in certain cases by simply using the power of data.

One significant advantage of leveraging data and software solutions for decarbonization is the relatively quick turnaround time. Companies can utilize enterprise software to start their emissions reduction goals in their existing operations and see results within a few quarters. This approach acts as a complementary measure to purchasing new equipment, which often requires significant time and capital expenditure to achieve emission reductions.

The role of data in the clean energy industry has undergone significant changes over the past several decades, and its trajectory for the future holds even more potential to help Minnesota achieve its clean energy goals. Over the last 20 years, the expansion of data utilization has been remarkable thanks in part to the substantial increase in the volume of available data. This expansion of data has opened doors to the application of Artificial Intelligence (AI) and Machine Learning (ML) techniques in the clean energy sector.

With a vast amount of data available from clean energy installations, algorithms can be trained to extract valuable insights and optimize clean energy solutions. For example, NextEra Energy Resources collects more than 20 billion datapoints a day about their own installations. This data informs their algorithms and gives their customers the ability to convert their energy data into actionable measures that facilitate the decarbonization as well as the optimization of their operations.

While state and federal policy is gradually increasing the minimum efficiency standards for equipment and building codes, there is still untapped potential for savings in system design and control choices of the current built environment. This is especially relevant for Minnesota when you take into account that combined, commercial buildings and residential structures account for more than 40% of Minnesota’s energy use. If industrial buildings are included in this calculation, the percentage skyrockets to 70. This presents a huge opportunity for leveraging data-driven insights to address these inefficiencies and further enhance energy efficiency across all sectors.

Looking ahead, the role of data in the clean energy industry is poised for continued evolution. The increasing availability and quality of data, along with advancements in AI and ML technologies, will enable more sophisticated analytics, predictive models, and optimization algorithms. This will drive further improvements in energy efficiency, better integration of renewable energy sources and the development of smart grids and intelligent energy systems.

Businesses can derive significant benefits from leveraging data insights in their operations by making informed decisions that drive improvements, save money, and decrease emissions.

For instance, businesses can utilize software to manage HVAC systems and thermostats, leading to optimized energy consumption and reduced operational costs. By implementing features like modulation temperature control, companies can achieve substantial energy savings, enhance efficiency, and start the process of planning for upgrades that require more significant capital investments.

The basic framework for getting started with data analytics is simple and effective.

1. Establish a baseline: Utilize historical data from utility bills to establish a baseline for energy consumption. This provides a clear starting point for measuring progress and identifying areas for improvement.
2. Benchmark: Compare energy usage with similar buildings or industry standards to gain insights into the potential for improvement. Benchmarking helps make sense of your performance relative to your peers and industry best practices.
3. Set goals: Based on the analysis of data and benchmarking, set realistic, and measurable goals for energy efficiency and emissions reductions.
4. Engage with experts: Seek guidance from energy professionals that can provide recommendations and insights to help identify specific changes and interventions that can optimize energy use and guide you on state and federal incentives available to assist in financing improvements.
5. Upgrade systems: Implement upgrades and improvements in energy-consuming systems based on data insights and recommendations from energy professionals. This may include equipment upgrades, installation of energy-efficient technologies or process optimization.
6. Measure and verify savings: Continuously measure and verify the net savings achieved through the implemented changes. This step ensures that the expected benefits are realized and allows for adjustments if needed.

The data sector offers a wide range of job opportunities that require diverse skill sets. An exciting piece of the ever-expanding clean energy industry is the diversity of jobs needed making it possible for a huge variety of skillsets and interests to be relevant in Minnesota’s clean energy future. NextEra Analytics utilizes roles in Applied Mathematics (Data Science), Traditional Sciences (e.g., Meteorology), Software Development, Project Management, and more.

EnergyPrint explained that 80% of commercial buildings in Minnesota are heated and cooled by rooftop units, but only 9% of these use an energy management system. The savings associated with an upgrade pay for the system in real time, but the industry is capacity constrained, desperately needing technical sales people and service technicians to realize this potential.

As the data sector continues to evolve and expand, these skills and job roles will remain in high demand.

To achieve the goals the state has set in the 100% Clean Energy by 2040 law and the Climate Action Framework, widespread adoption of data and analytics is crucial. By leveraging these tools, businesses can drive efficiency improvements and contribute to decarbonization efforts.

Software and data play a critical role in complementing capital expenditure installations. Businesses can utilize software solutions to optimize energy consumption, reduce emissions and improve operational efficiency while working on longer-term plans of acquiring more efficient hardware like HVAC systems and heat pumps. Software and data-driven efficiency improvements should work in tandem with hardware upgrades to maximize the overall impact on clean energy goals.

Increased emphasis on measurement, analysis, and goal setting is essential for driving demand for efficiency improvements. By utilizing data and analytics, businesses can measure their energy consumption, analyze patterns and trends and set ambitious goals for reducing emissions and increasing energy efficiency. This data-driven approach provides businesses with tangible metrics and benchmarks to track progress and drive continuous improvement.

As more businesses embrace data and analytics for energy management, it will drive a shift in resources to meet the expressed needs of customers. Increased demand for efficiency improvements will stimulate the market, leading to the development of innovative technologies, services and solutions that cater to businesses’ energy optimization requirements. This resource shift will foster a supportive ecosystem of energy professionals, service companies, engineers, suppliers, and utilities, all working together to serve businesses’ evolving needs.