ChronoPatternNet revolutionizes power forecasting using a unique 2D convolutional approach for advanced temporal pattern recognition. The 'chronocycle' hyperparameter, optimized via fast Fourier transform, structures 'Cyclical Time Frames,' enhancing both extraction and prediction accuracy. Integration of layer normalization and residual learning mitigates the vanishing gradient problem, ensuring stability. With superior efficiency, ChronoPatternNet achieves a reduction in the number of parameters ranging from 58.8% to 61.9% compared to existing models. This positions ChronoPatternNet as a significant advancement in real-time energy management.