Help demand side transformation, let AI modeling “get twice the result with half the effort”

INNPM22 With the deepening of the construction of new energy systems, especially on the demand side, the process of clean and low-carbon end-use energy is changing with each passing day, and AI technology is bound to become an indispensable support for promoting energy transformation.

An important reason is that most of the process processes that produce energy consumption, whether it is an industrial process such as manufacturing process, or the heating and cooling processes of buildings, need to achieve the reduction of energy consumption through modeling and continuous optimization, that is, only by establishing a process model can the key factors affecting energy consumption be analyzed from the principle, and abnormal energy consumption can be found and repaired. And develop a targeted response plan, so as to effectively reduce energy demand.

In many cases, however, building models based solely on the fundamental laws of physics has become impractical due to the complexity of the process. Taking the building industry as an example, to build a complete building energy consumption model, not only requires a large team of experts, but also needs to consider the details of the factors are vast.

INNPM22 In the face of such tricky modeling problems, artificial intelligence can “find another way” to provide more efficient solutions. For example, using machine learning technology in the field of artificial intelligence, you can start from the measured data and build a predictive model through learning, so as to achieve the effect of “twice the result with half the effort.”

Zhang Lei pointed out that at present, the application of AI technology to reduce energy consumption has been widely used in various industrial scenarios such as building heating and cooling, seawater desalination, home energy optimization or district heating systems. Over time, the role and effect of such applications in the energy transition is bound to be more widely and significantly played.