Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: The Importance of Environmental Factors in Forecasting Australian Power
  Demand   We develop a time series model to forecast weekly peak power demand for three
main states of Australia for a yearly time-scale, and show the crucial role of
environmental factors in improving the forecasts. More precisely, we construct
a seasonal autoregressive integrated moving average (SARIMA) model and
reinforce it by employing the exogenous environmental variables including,
maximum temperature, minimum temperature, and solar exposure. The estimated
hybrid SARIMA-regression model exhibits an excellent mean absolute percentage
error (MAPE) of 3.41%. Moreover, our analysis demonstrates the importance of
the environmental factors by showing a remarkable improvement of 46.3% in MAPE
for the hybrid model over the crude SARIMA model which merely includes the
power demand variables. In order to illustrate the efficacy of our model, we
compare our outcome with the state-of-the-art machine learning methods in
forecasting. The results reveal that our model outperforms the latter approach.

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The categories of this paper are: funct-an
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The categories of this paper are: comp-gas
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The categories of this paper are: eess
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The categories of this paper are: q-alg
9
The categories of this paper are: nlin
7
The categories of this paper are: hep-th
12
The categories of this paper are: nucl-th
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The categories of this paper are: q-fin
14
The categories of this paper are: math-ph
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The categories of this paper are: acc-phys
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The categories of this paper are: patt-sol
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The categories of this paper are: bayes-an
18
The categories of this paper are: econ
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The categories of this paper are: chem-ph
3
The categories of this paper are: physics
0
The categories of this paper are: hep-ph