Accurate analysis and prediction of building energy consumption is the basis for analyzing and formulating building energy efficiency plans. Nowadays, there are two main building energy analysis methods: building physical simulation and data-driven analysis.
- The dynamic physical simulation method, also known as the White-box Models, is mainly based on thermodynamics to perform detailed building thermal process modeling and simulation calculations for buildings and equipment systems, to obtain energy consumption and perform corresponding analysis. This method has been fully demonstrated in the past few decades and has become very mature. This kind of simulation has high accuracy and complexity, and the tools are often used in research fields, such as Energyplus. There are also some fast simulation software for local regulations on the market, such as TERMO. Their accuracy is not as high as Energyplus, but they can meet basic engineering requirements and can quickly generate corresponding reports such as APE in combination with local regulations.
- The data-driven method, this method is based on a large amount of existing historical monitoring data, through mathematical statistics and deep learning methods, to extract data characteristics for energy consumption analysis and prediction. This method came into being with the rapid development of machine learning algorithms and is an emerging data analysis method. These advantages and characteristics bring many possibilities to the application of data-driven methods in building energy consumption analysis.
These two methods have their own advantages and disadvantages. The physical simulation requires detailed building data and activity data, it also has very high requirements of the simulator. The data-driven method has lower requirements for users but requires a lot of historical data and mature algorithms. Many companies only use physical simulation to predict future energy consumption, because without long-term accumulation and energy platform develop/manage experience, data-driven analysis models are difficult to accurate prediction.
eFM has rich experience and advantages in both methods. eFM's Myspot platform manages buildings over 80M square meters all around world now, has rich experience in real estate, asset, facility management, energy management and governance. At the same time, eFM's energy and sustainability team also has rich experience in physical simulation, successfully helped big clients perform successful simulations for their energy operations. Nowadays, eFM also has rich experience for green building certification such as LEED. For eFM, the combination of the two is the best solution to customer needs.
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