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

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 averag...

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Vydáno v:Energies (Basel) Ročník 7; číslo 5; s. 2938 - 2960
Hlavní autoři: Bennett, Christopher, Stewart, Rodney, Lu, Junwei
Médium: Journal Article
Jazyk:angličtina
Vydáno: Basel MDPI AG 2014
Témata:
Accuracy
ARIMA with exogenous variables (ARIMAX)
autoregressive integrated moving average (ARIMA)
Capital expenditures
Control algorithms
Customers
Demand
Electricity
electricity demand
Energy management
Energy storage
forecast
Load
Low voltage
Mathematical models
Networks
neural network (NN)
Neural networks
Relative humidity
Residential
Scheduling
Time series
Variables
Queensland Australia
Australia
ISSN:1996-1073, 1996-1073
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Popis
Shrnutí: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.
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ISSN:1996-1073
1996-1073
DOI:10.3390/en7052938