Energies | |
Autoregressive with Exogenous Variables and Neural Network Short-Term Load Forecast Models for Residential Low Voltage Distribution Networks | |
Christopher Bennett2  Rodney A. Stewart1  | |
[1] Griffith School of Engineering, Griffith University, Gold Coast Campus, Southport Qld 4222, Australia; | |
关键词: forecast; electricity demand; residential; time series; autoregressive integrated moving average (ARIMA); ARIMA with exogenous variables (ARIMAX); neural network (NN); | |
DOI : 10.3390/en7052938 | |
来源: mdpi | |
【 摘 要 】
This paper set out to identify the significant variables which affect residential low voltage (LV) network demand and develop next day total energy use (NDTEU) and next day peak demand (NDPD) forecast models for each phase. The models were developed using both autoregressive integrated moving average with exogenous variables (ARIMAX) and neural network (NN) techniques. The data used for this research was collected from a LV transformer serving 128 residential customers. It was observed that temperature accounted for half of the residential LV network demand. The inclusion of the double exponential smoothing algorithm, autoregressive terms, relative humidity and day of the week dummy variables increased model accuracy. In terms of
【 授权许可】
CC BY
© 2014 by the authors; licensee MDPI, Basel, Switzerland.
【 预 览 】
Files | Size | Format | View |
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RO202003190026425ZK.pdf | 2076KB | download |