TY - GEN
T1 - Deep Learning Based Building Load Prediction for Residences of an Academic Institutions
AU - Ntsaluba, Sula
AU - Nwulu, Nnamdi
AU - Ntsaluba, Kuselo
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - Effective energy planning has in many instances been identified as a critical operation in order to achieve economic and sustainable building energy usage. Accurate building load prediction is a key factor that can result in energy usage and cost reduction. This paper presents an energy usage analysis for an academic institution over a period of four years. Building load prediction was then considered, where three different deep learning models were implemented in an attempt to identify the model that would perform best at predicting the load demand of a selected building over a 1 year period. The accuracy of the implemented models was evaluated through analysis of the Mean Absolute Percentage Error. The selected models produced values ranging from 0.09 to 0.22, which compare well with results highlighted in other literature studies for similar buildings.
AB - Effective energy planning has in many instances been identified as a critical operation in order to achieve economic and sustainable building energy usage. Accurate building load prediction is a key factor that can result in energy usage and cost reduction. This paper presents an energy usage analysis for an academic institution over a period of four years. Building load prediction was then considered, where three different deep learning models were implemented in an attempt to identify the model that would perform best at predicting the load demand of a selected building over a 1 year period. The accuracy of the implemented models was evaluated through analysis of the Mean Absolute Percentage Error. The selected models produced values ranging from 0.09 to 0.22, which compare well with results highlighted in other literature studies for similar buildings.
KW - building load prediction
KW - energy consumption
KW - feed forward neural network
KW - mean absolute percentage error
KW - recurrent neural network
UR - http://www.scopus.com/inward/record.url?scp=85127059123&partnerID=8YFLogxK
U2 - 10.1109/ICECET52533.2021.9698812
DO - 10.1109/ICECET52533.2021.9698812
M3 - Conference contribution
AN - SCOPUS:85127059123
T3 - International Conference on Electrical, Computer, and Energy Technologies, ICECET 2021
BT - International Conference on Electrical, Computer, and Energy Technologies, ICECET 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 International Conference on Electrical, Computer, and Energy Technologies, ICECET 2021
Y2 - 9 December 2021 through 10 December 2021
ER -