Managing a building's energy consumption is a complex task with numerous variables to consider. Fortunately, E360 offers a state-of-the-art Machine Learning and AI platform that simplifies the process. Our platform provides energy managers with valuable insights from data to help optimize energy consumption and reduce costs. With E360, you can confidently take control of your building's energy usage and make informed decisions. Don't rely on guesswork or outdated methods. Choose E360 and experience the power of our advanced Machine Learning and AI platform to achieve your energy management goals.
E360's AI and ML algorithms can detect patterns in building load and energy consumption increases against any data input source. Identify what's causing your building's energy consumption and optimize operations to correct wasted energy.
With machine learning-powered analytics, E360 can detect energy consumption patterns associated with avoidable system runtime and usage. Use these insights to optimize system settings and runtime, decreasing associated energy waste.
Benchmark and gain valuable insights into your building's energy consumption in relation to industry standards and best practices. Energy managers can utilize data-driven insights to identify areas to enhance energy efficiency and cost savings.
E360’s advanced reporting engine is fundamental to energy efficiency financing, energy performance management, GHG accounting efforts, and many government and utility programs.
Showcase your sustainability efforts and audit the effectiveness of energy programs with E360’s compliance and certification reporting. Gain insight into historical trends and data for maintaining Energy Star, LEED, and WELL building certifications.
With IPMVP accuracy, verify and quantify energy and demand savings for utility programs at the whole building, system, or equipment level by incorporating projects, measuring savings, forecasting results, and adjusting non-routine events (NRE).
E360's intelligent reporting enables you to compare billing and energy usage data against all available system variables and data points, including; environmental conditions, peak demand events, and more. Leverage these insights to find hidden correlations and new cost-saving opportunities.
Fill out our contact form, and one of our E360 experts will reach out shortly to help find the perfect energy management solution for your organization.
The International Performance Measurement and Verification Protocol, or IPMVP, is a non-prescriptive framework that provides a consensus approach to measuring and verifying efficiency investments. Its objective is to reduce barriers to the energy and water efficiency industries, and it is used broadly by energy services companies, utilities, government agencies, building managers, manufacturing managers, and industrial managers. The IPMVP offers a consistent approach to measuring and verifying carbon emissions reduction in various energy sectors. As primarily a non-prescriptive framework, the IPMVP provides an overview of M&V's current best practices while remaining flexible.
Measurement and Verification, or M&V, is the process of quantifying energy savings from efficiency projects by collecting data on energy consumption before and after implementing energy-saving measures. Measurement and verification reporting help assess the project's success and identify opportunities for further improvements. Accurate measurement and verification can lead to significant cost savings and environmental benefits.
Energy is measured in units of Joules (J), which is the basic unit of energy in the International System of Units (SI). Additionally, some industries may use units such as British Thermal Units (BTUs) or electronvolts (eV) to measure energy.
Machine Learning, or ML, is a type of artificial intelligence that involves training computer system algorithms to learn and improve from data inputs without being explicitly programmed. ML is based on the idea that machines can be trained to recognize patterns and make decisions based on that data. Machine Learning involves algorithms that can learn from data, identify patterns, and make predictions or decisions based on that learning.
Machine Learning has greatly contributed to the improvement of energy efficiency. By analyzing large amounts of data, machine learning algorithms can identify patterns and insights that humans may miss. This allows for more precise predictions and better decision-making regarding managing energy usage. For example, machine learning can be utilized to optimize heating, ventilation, and air conditioning systems in buildings, leading to significant energy savings. It can also help to identify areas of energy waste and suggest strategies for reducing consumption. Overall, machine learning has the potential to revolutionize the way we use energy and achieve a more sustainable future.
All organizations face a common challenge: How do you keep your buildings energy efficient, healthy, while optimizing your operations. Sanalife’s E360 provides a solution to help organizations of all types to conserve essential resources, promote sustainability, and increase productivity.
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