摘要：The over-population and the limited energy resources have puzzled the government and private organizations to think about the providence of non-stop energy resources to the industries, hospitals, smart homes, and shopping malls to ensure normal routine activities. To accept this challenge, some researchers put their efforts into generating energy from renewable energy resources (solar, fossil fuels, wind turbines, geothermal energy, and many others) to fulfill the needs of life. While some researchers worked on the efficient utilization of the available energy resources to save the energy for future generations. Inspiring from the second approach, this research work has proposed a systematic allocation of energy resources using the slice-based mechanism in a smart grid environment. This research framework using a hybrid model comprises long short-term memory (LSTM), and a support vector machine (SVM), where the LSTM classifies different energy requests (for allocation of energy resources) while the SVM accomplishes the statistical analysis (to estimate the number of solar energy resources allocated and for a specific interval of time). This need-based allocation of energy resources will not only assist in saving energy resources for future use, but will also improve the life of the power grid and other electric appliances (due to over-usage and burning). The applicability of this model is validated by testing it on a real-time scenario like slice failure conditions, slice overflow conditions, a huge number of requests, and alternate slice allocation conditions. Furthermore, the incoming request classification is also validated based on its accurate identification using a confusion matrix, varying number of hidden layers, accuracy, and time consumption. The outperformance of the selected based on these scenarios and validation metrics reflects the applicability of this framework. Moreover, this framework will assist in reducing overbilling charges and energy savage for future generations due to its need-based allocation of energy resources assignment capabilities.
