Paper Title
Household Energy Management Based On Dynamic Pricing Using Extreme Learning Machine

Abstract
The goal of energy management is to utilize energy effectively and efficiently without reducing energy quality. This application must have the ability to regularly record and evaluate, schedule, and can provide advice on the energy use required. In this paper, household loads are used to implement energy management applications. This energy management has a Load scheduling feature. This feature is used to implement Dynamic Pricing. Household electrical loads will be grouped based on energy consumption and loads, ie inelastic loads, flexible elastic loads, flexible running loads, and flexible elastic loads. After classifying the load, Extreme Learning Machine (ELM) is used to predict the upcoming electricity prices. From the simulation results, the accuracy and speed of ELM are better than artificial neural network (ANN) with a Mean Absolute Percentage Errors (MAPE) of 1.54% and learning speed with a maximum time of 0.1081 seconds. The price Prediction produced by ELM, as well as load scheduling, can help user to find out the electricity cost usage and the right time to use electrical loads. Keywords - Energy Management, Dynamic Pricing, Extreme Learning Machine, Load Scheduling