Electricity consumption forecasting models for administration buildings of the UK higher education sector

By K. P. Amber, Muhammad Waqar Aslam, S. K. Hussain. 2015

132 citations recorded by the source corpus.

Subfields: Building and Construction, Electrical and Electronic Engineering, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/cf2d2d69846341fd646aad88871beaada7b099a4

How researchers see this paper

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

In Building and Construction

1. Sung-Bae Cho
2. T. Kim

In Electrical and Electronic Engineering

1. Sung-Bae Cho
2. Ljupco Kocarev
3. T. Kim
4. Sonja Filiposka
5. Aleksandar Dedinec
6. Aleksandra Dedinec

In Management Science and Operations Research

1. Sung-Bae Cho
2. Ljupco Kocarev
3. T. Kim
4. Sonja Filiposka
5. Aleksandar Dedinec
6. Aleksandra Dedinec

Citations in the corpus

• Predicting residential energy consumption using CNN-LSTM neural networks (2019, methodology): “Amber predicted daily electricity consumption by applying genetic programming to multiple linear regression [28].”
• Deep belief network based electricity load forecasting: An analysis of Macedonian case (2016, background): “[18,19], or it may be applied to smaller scale such as local areas in a country [20], a region [21,22], a city [23], a campus [24,25], in microgrid environments [26] or a household [27,28], which is one of the fundamental topics in smart grids.”