Medium and Short Term Energy Forecasting using LSTM Neural Network Method for Gujarat State
A stable power system requires balance between generation and demand. The power
system under World Bank initiative has restructured in majority of countries throughout the …
system under World Bank initiative has restructured in majority of countries throughout the …
LSTM-based electrical load forecasting for Chattogram city of Bangladesh
Load forecasting is one of the necessary tools for the modern energy management system.
In the smart grid, electric load forecasting offers a useful duty in decision making for power …
In the smart grid, electric load forecasting offers a useful duty in decision making for power …
Short-Term Electrical Load Prediction for Future Generation Using Hybrid Deep Learning Model
SMA Haque, GC Sarker… - … on Advancement in …, 2022 - ieeexplore.ieee.org
Power generation is increasing worldwide every year to cope with ever-increasing energy
demand. Therefore, a significant necessity exists for forecasting the load demand to manage …
demand. Therefore, a significant necessity exists for forecasting the load demand to manage …
Short-Term Load Forecasting Using Artificial Neural Network and Time Series Model to Predict the Load Demand for Delhi and Greater Noida Cities
N Singh, P Sharma, N Kumar, M Sreejeth - Proceedings of 6th …, 2021 - Springer
Forecasting of load refers to prediction of power demanded by the targeted geographical
area based on the trends and patterns of previous load demands. To forecast the load …
area based on the trends and patterns of previous load demands. To forecast the load …
Load forecasting and analysis of power scenario in bihar using time series prediction and machine learning
The present paper is regarding the reliable forecasting of electrical load demands of the
Indian state of Bihar using the long short-term memory (LSTM) technique of machine …
Indian state of Bihar using the long short-term memory (LSTM) technique of machine …
Short time load forecasting for Urmia city using the novel CNN-LTSM deep learning structure
YK Ahranjani, M Beiraghi, R Ghanizadeh - Electrical Engineering, 2024 - Springer
In the present time, electricity stands as one of the most fundamental needs within human
societies. This is evident in the fact that all industrial activities and a significant portion of …
societies. This is evident in the fact that all industrial activities and a significant portion of …
Performance analysis of machine learning techniques for load forecasting
Load forecasting is a key element in the performance of an electrical power system because
of the associated economic constraints. The amount of load consumed depends on a …
of the associated economic constraints. The amount of load consumed depends on a …
On short-term load forecasting using machine learning techniques and a novel parallel deep LSTM-CNN approach
Since electricity plays a crucial role in countries' industrial infrastructures, power companies
are trying to monitor and control infrastructures to improve energy management and …
are trying to monitor and control infrastructures to improve energy management and …
Very short-term load forecasting with deep learning neural network in Delhi, India
Efficient management of electric power grid requires accurate prediction of load demand.
Due to the large training data and complex load patterns, traditional models fail to predict …
Due to the large training data and complex load patterns, traditional models fail to predict …
Time series forecasting of electrical energy consumption using deep learning algorithm
EO Edoka, VK Abanihi, HE Amhenrior… - Nigerian Journal of …, 2023 - ajol.info
Energy consumption forecasting is an operation of predicting the future energy consumption
of electrical systems using previous or historical data. The Long Short-term Memory (LSTM) …
of electrical systems using previous or historical data. The Long Short-term Memory (LSTM) …