In recent years, the photovoltaic (PV) electricity production sector has grappled with the formidable challenge of solar illumination variability. This variability, which is contingent upon factors such as date, location, and time, is chiefly attributable to atmospheric cloud cover. Solar energy forecasting proves invaluable for optimizing the operation of intelligent inverters, advanced energy storage systems (batteries), and sophisticated transformers within photovoltaic installations. This forecasting technique relies on image processing technology, facilitated by the cost-effective deployment of ground-based cameras. This work presents an intelligent energy control and management system using an artificial vision system. This system combines three automatic controls. First, the Maximum Power Point Tracking (MPPT) command with the use of the Fuzzy Logic Control (FLC) algorithm to operate the Photovoltaic generator at its maximum power. Second, the forecast of energy that depends on the cloud cover in the sky is done by detecting clouds in real-time, based on digital image processing using a camera. A variable threshold algorithm has been developed. Third, the distribution of the energy stored in the battery in priority order ensures continuity of energy to the load and ensures the longest possible discharge time for the battery array.