# Forecasting Hourly Energy Consumption Using LSTM: A Deep Learning Approach for Indian States

**Type:** Papers  
**Canonical URL:** https://scholariq.org/papers/forecasting-hourly-energy-consumption-using-lstm-a-deep-learning-approach-for/

## Facts

| Field | Value |
| --- | --- |
| Author Names | G. Vishnu Priya,A. Srujanajyothi -,Vijaya Lakshmi |
| Citations | 0 |
| DOI | 10.71097/ijsat.v16.i3.7615 |
| Fields | Energy,Engineering |
| Open Access | true |
| OA Status | hybrid |
| OA URL | https://www.ijsat.org/papers/2025/3/7615.pdf |
| OpenAlex ID | https://openalex.org/W4413131664 |
| Type | article |
| Year | 2025 |

## Paper authors

- [Vijaya Lakshmi](https://scholariq.org/researchers/vijaya-lakshmi/)

## Paper journal

- [International Journal on Science and Technology](https://scholariq.org/journals/international-journal-on-science-and-technology/)

## Paper primary topic

- [Energy Load and Power Forecasting](https://scholariq.org/topics/energy-load-and-power-forecasting/)

## Paper topics

- [Energy Load and Power Forecasting](https://scholariq.org/topics/energy-load-and-power-forecasting/)
- [Energy, Environment, and Transportation Policies](https://scholariq.org/topics/energy-environment-and-transportation-policies/)
- [Energy Efficiency and Management](https://scholariq.org/topics/energy-efficiency-and-management/)

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Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.