Investment benefit evaluation method based on feature extraction and lasso regression

Abstract

The invention provides an investment benefit evaluation method based on feature extraction and lasso regression. According to the method, an investment index and power supply reliability index feature pool is constructed, an investment index associated with each reliability index is screened, a lasso regression association model is constructed based on the screened power supply reliability index and investment index combination, and the investment benefit is evaluated based on the lasso regression association model. The method can achieve precise and objective evaluation of the benefit of the investment project of the power distribution network, and guarantees high efficiency and precision of investment construction of the power grid.

Description

technical field [0001] The invention relates to the field of electric power, in particular to an investment benefit evaluation method based on feature extraction and lasso regression. Background technique [0002] The investment and construction of distribution network is an important measure to ensure the electricity demand of industry and residents. With the continuous increase of investment in projects at all levels, objective and reasonable evaluation of the benefits generated by each investment project is an important part of the work to promote the development of power grid construction in a scientific, environmentally friendly and sustainable direction. Through the reliability and benefit evaluation of distribution network investment and construction, it is possible to quantitatively discover the degree of improvement of distribution network reliability by different investment projects, and find the core investment projects that can effectively improve the reliability...

AI Technical Summary

Problems solved by technology

[0003] At present, the research on the benefit evaluation of distribution network investment and construction focuses on qualitative and quantitative evaluation methods, involving multiple dimensions such as economic benefits, environmental benefits, and social benefits. The main methods include analytic hierarchy process, expert scoring method, gray level Models, etc., but some methods involve too many index systems and too many evaluation dimensions, resulting in excessive volatility of the evaluation method itself, and the evaluation results may be distorted or not robust; most methods lack sufficient objectivity, All of them have more subjective evaluation standards. Although expert opinions are adopted, they will still be affected by the knowledge, experience and preferences of experts, which has a greater chance; in general, the existing methods do not make enough use of objective data and information, and are relatively inefficient. It is difficult to obtain a targeted and specific evaluation model

The few evaluation methods based on historical investment data are often limited by insufficient effective data and too many indicators to be evaluated, making it difficult to form a comprehensive evaluation model

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