In order to set up a leading power station detection system of the industry, AKCOME established an all-round power station monitoring system, and became the first in the industry to establish cooperation partnership with Huawei, jointly making breakthroughs in advanced technical fields of Internet of Things and cloud service; AKCOME will adopt the method of factor analysis to observe development status of the industry, diagnose technical level of power stations and monitor projects’ risk factors, thus establishing a professional and accurate power station rating frame. The rating frame of AKCOME consists of three models, that is, spider diagram scoring, overall rating and IRR value assessment; rating results will be presented by means of quantization, systematization and multi-dimensional assessment of risk factors combining with three assessment methods of online model, online data and offline technology; meanwhile, cash flow analysis will be carried out, and the rating results will be tested by stress testing.

• Model of spider diagram scoring: carrying out quantization and assessment of risk factors of each project from five dimensionalities based on two major aspects of generating capacity and power station. Scoring of generating capacity includes five dimensions, that is, project quality, irradiation, power grid, value of land sources, fluctuation rate, design, operation and maintenance; and scoring of power station includes five dimensionalities, that is, power station quality, stress testing, finance, power generation and consumption.
• Model of overall rating: carrying out overall rating of each power station by establishing a weight assessment model according to all relevant factors.
• Model of IRR value assessment: carrying out rating of asset value of each power station from the perspective of investment income by IRR and ROE asset assessment model established by collecting and analyzing all relevant data.
• Cash flow analysis: establishing cash flow model according to known risk factors, predicting cash flow by predicting generating capacity of a power station, and accordingly calculating IRR, NPV and ROE parameters of the asset; referring to historical data of generating capacity of the power station, and predicting statistic data, fluctuation rate and distribution of cash flow by applying the method of Monte Carlo Simulation, thus assessing income risks.