Deep Learning Based Load Forecasting for Futuristic Sustainable Smart Grid
- Resource Type
- Conference
- Authors
- K.S., Prajwal; Amitasree, Palanki; Vamshi, Guntha Raghu; Devi, V. S. Kirthika
- Source
- 2022 International Conference on Electronics and Renewable Systems (ICEARS) Electronics and Renewable Systems (ICEARS), 2022 International Conference on. :35-40 Mar, 2022
- Subject
- Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Deep learning
Earth
Load forecasting
Voltage
Transforms
Logic gates
Transformers
Smart Energy Meter
Deep Learning
Correlation
LSTM
GRU
Root Mean Square Error
- Language
Power utilization has expanded dramatically during the previous few decades. This expansion is intensely troubling the power merchants. Subsequently, anticipating the future interest for power utilization will give an advantage to the power wholesaler. Anticipating power utilization requires numerous boundaries. This work presents two approaches with one using a deep learning based Long Short-Term Memory(LSTM) and Gated Recurrent Unit for short term load forecast These models consider the previous electricity consumption to predict the future electricity consumption. The data for modelling is taken from London Smart Energy Meter dataset. The performance of the models was evaluated against the root mean sqaure error to check the best method that can be utilized in load forcasting.