Research on Multi-Channel Vision Platform for Intelligent Robots Based on Deep Learning

Load forecasting plays an indispensable role in ensuring the stability and economy of power supply. This study aims to deeply analyze the application of soft computing technology and data mining theory in the field of power system load forecasting. By introducing neural networks, especially Kohonen network and BP neural network, this study successfully realizes the robust clustering of load curves, the construction of feature curves, and the identification of abnormal curve patterns. Meanwhile, this paper combines the composite neural network and feature curves to effectively recognize and correct the abnormal data. In the field of fuzzy modeling, this study optimizes the structure of the fuzzy inference system using the CART algorithm and establishes a multi-parameter load forecasting model. Case studies confirm the effectiveness of these methods and highlight the strong potential of soft computing and data mining in improving load forecasting accuracy and data quality.