A hybrid neuro-fuzzy inference system-based algorithm for time series forecasting applied to energy consumption prediction

•A neuro-fuzzy inference system boosted with a recent optimizer is proposed.•A new layer denominated as non-working time adaptation was proposed.•Autoregressive process is applied to generate significant endogenous inputs.•Hourly energy consumption of a Faculty building is accurately predicted. The...

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Vydané v:Applied energy Ročník 268; s. 114977
Hlavní autori: Jallal, Mohammed Ali, González-Vidal, Aurora, Skarmeta, Antonio F., Chabaa, Samira, Zeroual, Abdelouhab
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Elsevier Ltd 15.06.2020
Predmet:
algorithms
ANFIS
Autoregressive process
Building energy consumption
buildings
California
data collection
energy
fuzzy logic
gender differences
Gender-difference firefly algorithm
prediction
Spain
time series analysis
Time series forecasting
California
Spain
Time series forecasting
Gender-difference firefly algorithm
Autoregressive process
ANFIS
Building energy consumption
ISSN:0306-2619, 1872-9118
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Shrnutí:•A neuro-fuzzy inference system boosted with a recent optimizer is proposed.•A new layer denominated as non-working time adaptation was proposed.•Autoregressive process is applied to generate significant endogenous inputs.•Hourly energy consumption of a Faculty building is accurately predicted. The accuracy of the prediction of buildings’ energy consumption is being tackled using existing artificial intelligence techniques. However, there is a lack of effort on the development of new techniques for solving that problem and, therefore, achieving higher performance, which is important for the efficient management of energy in many levels. This study addresses this gap by proposing a new hybrid machine learning algorithm that incorporates the adaptive neuro-fuzzy inference system model with a new version of the firefly algorithm denominated as the gender-difference firefly algorithm. We expanded the search space diversification to increase the accuracy on the prediction and adopted the autoregressive process in order to approximate the chaotic behavior of the consumption time series. A new layer, denominated as non-working time adaptation was also integrated so as to decrease the fast variability of the predictions during non-working periods of time. We have applied our algorithm for the consumption prediction on 1 h, 2 h and 3 h ahead horizons. We have obtained improvements on the MAPE and R coefficient when compared with state-of-the-art publications in both a private dataset from the Faculty of Chemistry, located in the city of Murcia, Spain and a public dataset of the consumption of a Retail building located in California, United States. We also show our method’s performance in five more buildings. Our results demonstrate the robustness and the accuracy of our proposal when compared to the traditional adaptive neuro-fuzzy inference system models and also to the different predictive techniques implemented in several pieces of literature.
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ISSN:0306-2619
1872-9118
DOI:10.1016/j.apenergy.2020.114977