The impact of e-commerce on the innovation and advancement of new energy products

During the past decade, the magnitude and speed of technological change have produced a revolution in business as well as in retail. The development has been facilitated by the online sales of applications that incorporate solar thermal collectors into their customisations. For energy users, such an application is not just a commercial opportunity but also a sustainable, workable, and more digitalised solution. This research introduces a theoretical analysis technique that examines how energy efficiency characteristics affect the development of energy goods, taking into account the impact of e-commerce. Additionally, the technique uses a deep learning model for variable prediction. This study also explains how the rise of e-commerce may lower energy consumption per GDP unit, improve energy production efficiency, and control the uncontrollably high increase in overall energy consumption. Last but not least, the experiment's analysis reveals that, for iteration 90, the Deep Recurrent Neural Network (DRNN) obtained a minimum MAE of 0.107, the LSTM a maximum MAE of 0.209, the GRU a minimum MAE of 0.186, and the MLP a minimum MAE of 0.144.