Name
Title | ||
|---|---|---|
A comparative study on neural network-based prediction of smart community energy consumption |
Abstract | ||
|---|---|---|
This paper presents a comparative study on designing accurate prediction of future energy consumption at both the household level and the community level. Different Neural Network (NN), including conventional NN, Deep Neural Networks (DNN), and Sliding Window Neural Networks (SWNN), are compared in this work, where a SWNN uses a window of historical data to predict the future energy consumption. Our experimental study shows that the conventional NN can achieve high accuracy in prediction while deep NN does not generate better results. Through data normalization and temporal relationship exploration, SWNN becomes superior to conventional methods and achieves above 99.5% accuracy with a more condensed error distribution. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/UIC-ATC.2017.8397441 | 2017 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computed, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI) |
Keywords | Field | DocType |
Sliding Window Neural Networks,Deep Neural Networks,Energy Consumption,Prediction,Smart Community | Data mining,Renewable energy,Sliding window protocol,Computer science,Smart community,Artificial neural network,Energy consumption,Deep neural networks,Database normalization,Distributed computing | Conference |
ISBN | Citations | PageRank |
978-1-5386-1591-1 | 0 | 0.34 |
References | Authors | |
4 | 5 | |