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

Energies

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 R² 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.

【 授权许可】

CC BY   
© 2014 by the authors; licensee MDPI, Basel, Switzerland.