Artificial Intelligence Techniques Based Energy Prediction Analysis and Load Forecasting Using a Dynamic Website

Electricity plays a crucial part in human living. The use of electricity has become a necessity. Though energy is a renewable resource it is being wasted often due to energy leakage, short circuit, and load balancing. Numerous actions and choices, including the use of load prediction affect countless decisions and actions, including transportation, unit involvement, fuel reservation, and out-of-service network study. Energy consumption data of a building is generated via an energy meter; it is then trained to fewer models for prediction and forecasting. Energy usage forecasting using LSTM with a higher accuracy of 87% than other Autoregressive models and with a Mean Absolute Error of 0.03 is suitable for implementation. Then the model is built into a Web App that enables users to keep track of the energy usage of their appliances. In the next step a threshold value is set, and whenever the appliances reach the maximum threshold value it is notified to the user or consumer which helps in reducing unwanted energy wastage in any particular area of use.