The origin of this technique is to investigate how to combine the power generation data, meteorological data (such as surface radiation and wind speed) and the Weather Research and Forecasting Model (WRF) data to establish the power generating forecast system for ensuring the credibility of the Taiwan Renewable Energy Certificate issuance. 
 
The most important breakthrough of this technology is the use of WRF grid meteorological numerical data (such as daily radiation, etc.) at any latitude and longitude of 3 km resolution. The system will detect the WRF data on the grid points closest to the target site and use interpolation algorithms to interpolate the relevant WRF variables to the target site, which can reduce the cost of installing environmental sensors and replace physical environmental sensors in a more efficient way to obtain accurate estimates of meteorological and power generation data of the target site.

This technology acted as pioneer of the international Renewable Energy Certificate system in proposing the meteorological information in combination with the IoT technology for ensuring the credibility of Renewable Energy Certificate mechanism. This technology use the weather forecast variables output by the Weather Research and Forecasting Mode (WRF) and interpolate to power generation site of any latitude and longitude and use artificial intelligence to conduct prediction and analysis of power generation at the site, combined with intelligent geographic information system for electricity monitoring and resource risk assessment at the site, can not only reduce the installation cost of physical sensors but also ensure the credibility of Taiwan Renewable Energy Certificate Mechanism.

The analysis of energy variability in the power site is an important tool for making energy related decisions. In the liberalized electricity market, electricity forecast data is provided by electricity purchase contracts or bidding mechanisms as a reference for participation in the market. The developed monitoring system can provide reliable energy resource capacity analysis and risk management.