Electricity Consumption Forecasting System Using Deep Learning

Abstract

Energy modeling in Smart Buildings (SB) and planning and operating the generation of power based on the information extracted are key components of the Smart Grid’s (SG’s) energy management system. In the world, buildings use a significant amount of energy that contributes to energy efficiency programs. Additionally, excessive utilization power generation appliance also including air conditioners and heater, breathing, and climate control (HVAC) units, improper microclimate control, and inappropriate start-up and ordering of power equipment waste a lot of heat pumps. The utility can mitigate energy generation costs when it anticipates electrical loads and schedules generation resources in accordance with the demand. To estimate electricity usage at varying tiers of utility grid systems, a range of techniques have already been used. The goal of this study will be to create a hybrid deep learning model can predict resource utilization in infrastructures. Model building and data cleaning are the two stages of the proposed framework. Data cleaning involves pre-processing techniques and adding additional lag values to raw data. This hybrid deep learning (DL) approach is made up of a series of completely connected layered and linear Long Short-Term Memory (LSTM) sections layered over bi-directional Long Short-Term Memory (LSTM) components and is based also on collected information. You could incorporate the dependence structure of electricity usage on regressors and increase computation efficiency, training time, as well as computational complexity by using the results obtained.