Load forecasting techniques and methodologies: A review

By Arunesh Kumar Singh, Ibraheem, S. Khatoon, Md. Muazzam, D. K. Chaturvedi. International Conference on Power, Control and Embedded Systems, 2012

175 citations recorded by the source corpus.

Subfields: Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/b024fcb6d542acc6ca5a0f11fa3f6471a3b5199e

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Electrical and Electronic Engineering

1. Milos Manic
2. Kasun Amarasinghe
3. Daniel L. Marino
4. Kafait Ullah
5. M. Imran
6. Zafar Ali Khan
7. Mohammed A. Alghassab
8. Aneeque A. Mir

Citations in the corpus

• Deep neural networks for energy load forecasting (2017, methodology): “Readers are referred to [17], [18] and [4] for comprehensive surveys of different techniques used for load forecasting.”
• A Review of Electricity Demand Forecasting in Low and Middle Income Countries: The Demand Determinants and Horizons (2020, background): “These statistical models are useful when a dynamic series needs to be converted to a stationary form using the process of differentiation [71].”
• Building energy load forecasting using Deep Neural Networks (2016, methodology): “For surveys of different techniques used for load forecasting, readers are referred to [16], [17] and [8].”