Autoregressive with Exogenous Variables and Neural Network Short-Term Load Forecast Models for Residential Low Voltage Distribution Networks

Christopher Bennett, Rodney A. Stewart and Junwei Lu
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Christopher Bennett: Griffith School of Engineering, Griffith University, Gold Coast Campus, Southport Qld 4222, Australia
Rodney A. Stewart: Griffith School of Engineering, Griffith University, Gold Coast Campus, Southport Qld 4222, Australia
Junwei Lu: Griffith School of Engineering, Griffith University, Gold Coast Campus, Southport Qld 4222, Australia

Energies, 2014, vol. 7, issue 5, 1-23

Abstract: 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 R 2 and for each modelling technique and phase, NDTEU hindcast accuracy ranged from 0.77 to 0.87 and forecast accuracy ranged from 0.74 to 0.84. NDPD hindcast accuracy ranged from 0.68 to 0.74 and forecast accuracy ranged from 0.56 to 0.67. The NDTEU models were more accurate than the NDPD models due to the peak demand time series being more variable in nature. The NN models had slight accuracy gains over the ARIMAX models. A hybrid model was developed which combined the best traits of the ARIMAX and NN techniques, resulting in improved hindcast and forecast fits across the all three phases.

Keywords: forecast; electricity demand; residential; time series; autoregressive integrated moving average (ARIMA); ARIMA with exogenous variables (ARIMAX); neural network (NN) (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2014
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (20)

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