Made Adi Paramartha Putra
- Performance Comparison of Named Data Network IoT (NDNIoT) and IP Based IoT using Least Recently Frequently Used Replacement Policy
- Smart grid for Energy consumption prediction using LSTM in Industrial Environment
-Literature Review:
a. A Multidirectional LSTM Model for Predicting the Stability of a Smart Grid
b. Electric Load Forecasting in Smart Grid Using Long-Short-Term-Memory based Recurrent Neural Network
-Elaborate how LSTM Model & Algorithm works in order to predict & forecast Energy Usage
-Search and preparing the Dataset
-Literature Review:
a. Short-Term Residential Load Forecasting based on LSTM Recurrent Neural Network
b. A Novel Hybrid Short-Term Load Forecasting Method of Smart Grid using MLR and LSTM Neural Network
-Prepare Energy Usage Dataset
Smart Grid for Energy Consumption Prediction using LSTM in Industrial Environment
-Search for some Energy Usage Data
-Dataset Preparation
- Search and review more papers related to LSTM and Smart Grid
-Able to simulate DL with a modified model
-Design the proposed solution of Smart Grid Energy into an LSTM simulation
- Accepted paper in Local & International Conference
- Accepted journal indexed by SCI