Research on Investment Effect Evaluation of Power Grid Companies Based on Deep Learning

The scientific nature of investment decisions in the power industry directly affects power grid safety and resource allocation efficiency. Facing the complexity of the construction of a new power system and the rapidly evolving policy environment, traditional evaluation methods relying on static financial indicators and manual experience have significant limitations. This study proposes a dynamic evaluation model that integrates the Bidirectional Long Short-Term Memory network (Bi-LSTM) and the multi-head attention mechanism. By integrating multi-source time-series data such as macro-economic indicators, power grid operation parameters, and investment project characteristics, a cross-cycle quantitative evaluation framework for power grid investment effects is constructed. The research results provide a quantitative decision-making tool for the formulation of differentiated investment strategies for provincial power grids, further promoting the transformation of asset life-cycle management towards dynamism and refinement.