In power stations, AI models are deployed on edge devices for photovoltaic power generation forecasting and they usually encounter limited and shared computational resources. Our invention scales deep neural networks at the block granularity, thus maximizing the forecasting accuracy per unit of computational resources. Our deployment in real world environment demonstrates its effectiveness in reducing annual carbon emission by 128,000 tongs. Our future work is to apply block-grained model scalin