Intelligent physical systems for strategic planning and management of enterprise information

Abstract

Recently developed advanced metering infrastructure in an intelligent physical system (IPS) sorts large quantities of data available in design, whereas the potential electricity system is implemented for profits and aid in the client’s transition from inactive to an active role. This paper investigates the usage of Deep Reinforcement Learning (DRL), in Intelligent Physical System for Strategic Planning in Enterprise Information (IPSSPEI) has been proposed to achieve the online schedule optimization for building energy management of Enterprise Data in an intelligent grid context. Here, two methods such as, profound R-learning and deep policy gradient, have been proposed to compute and examine the learning procedure which performs several actions simultaneously to overcome the scheduling problems. Hence, this high-dimensional database contains information about the generation and utilization of energy by photovoltaic power cars and smart buildings, with advanced metering infrastructure. Moreover, the electrical energy forecasting approaches could be used to give a real-time input to the consumers, facilitating the better application of electricity in an intelligent physical system to overcome the scheduling problems.