Analyzing the impact of weather variables on monthly electricity demand

By Ching-Lai Hor, Simon J. Watson, S. Majithia. IEEE Transactions on Power Systems, 2005

280 citations recorded by the source corpus.

Subfields: Electrical and Electronic Engineering, Renewable Energy, Sustainability and the Environment

View Paper: https://www.semanticscholar.org/paper/d3acecfe7aa6a381d3890f2f66440b8517da25ba

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. Kashem M. Muttaqi
2. James W. Taylor
3. Ashish Prakash Agalgaonkar
4. Dao H. Vu
5. Rob J Hyndman
6. Ralph D. Snyder
7. Alysha M. De Livera
8. Grzegorz Dudek

In Renewable Energy, Sustainability and the Environment

1. Kashem M. Muttaqi
2. James W. Taylor
3. Ashish Prakash Agalgaonkar
4. Dao H. Vu

Citations in the corpus

• Electric load forecasting methods: Tools for decision making (2009, unknown): “The comparison was based on two time series: the hourly demand for Rio de Janeiro in 1996 (30 weeks, 5th May 1996–30th November 1996) and the half-hourly demand for England and Wales in 2000 (30 weeks, 27th March–22nd October).”
• A variance inflation factor and backward elimination based robust regression model for forecasting monthly electricity demand using climatic variables (2015, methodology): “In this timeframe, multiple regression model performs comparatively better than 49 the commonly used models such as artificial neural network (ANN), Socioeconomic (S-E), and Box and Jenkins (B&J) models 50 as reported in [15].”
• Triple seasonal methods for short-term electricity demand forecasting (2010, background): “In multiple years of load data, in addition to intraday and intraweek cycles, an intrayear seasonal cycle is also apparent.”