Datasets & Models

Dataset¶

The available datasets are listed in the data folder, with metadata provided in data/metadata.xlsx. Below are some of the datasets currently included in the library:

Ausgrid Solar Home Datasets¶

This dataset has been widely used in net load forecasting research since 2016. It includes data from 300 solar-equipped households within the Ausgrid network in Sydney. The original source is here.

Dataset Name	Description
`ds1_ahsd.csv`	Ausgrid Solar Home Dataset, aggregate of 300 household data in Ausgrid network
`ds4_ashd_with_weather.csv`	`ds1` enhanced with temperature, relative humidity, and wind speed data
`ds13_ashd_with_cloud_solcast.csv`	`ds4` further enriched with cloud data from Solcast

Australia Energy Data Platform (AEDP) Datasets¶

These datasets were compiled by UNSW Sydney using data from Solar Analytics and Wattwatchers. Sensitive information such as customer addresses, names, and NMIs has been removed. The original source is here.

Dataset Name	Description
`ds10_aedp_cluster2_30min.csv`	AEDP dataset for Cluster 2 with 30-minute resolution
`ds11_aedp_cluster2_30min_with_weather.csv`	`ds10` enhanced with weather data including temperature, humidity, etc.

Ausgrid Zone Substation (ZS) Datasets – Mascot¶

Unlike the previous household-focused datasets, this dataset covers a zone substation, which includes residential, commercial & industrial (C&I), and major customers. The original source is here.

Dataset Name	Description
`ds14_ausgrid_zs_mascot.csv`	Zone Substation data for Mascot
`ds15_ausgrid_zs_mascot_30min_with_weather.csv`	`ds14` enhanced with weather data at 30-minute resolution

Model¶

Forecasting Models Overview¶

Model ID	Model Name	Short Description
`m1_naive`	Naive	Forecast equals the last observed value
`m2_snaive`	Seasonal Naive	Forecast equals the value from the same season in the previous cycle

Statistical Models¶

Model ID	Model Name	Short Description
`m3_ets`	ETS	Exponential smoothing model with error, trend, and seasonality components
`m4_arima`	ARIMA	Autoregressive Integrated Moving Average model for time series forecasting
`m5_sarima`	SARIMA	Seasonal ARIMA model with seasonal components
`m6_lr`	Linear Regression	Predicts future values using a linear combination of input features

Machine Learning Models¶

Model ID	Model Name	Short Description
`m7_ann`	ANN	Basic Artificial Neural Network with one hidden layers
`m8_dnn`	Deep Neural Network	ANN with more than one hidden layer
`m9_rt`	Regression Tree	Decision tree model for regression tasks
`m10_rf`	Random Forest	Ensemble of regression trees for improved accuracy and robustness
`m11_svr`	Support Vector Regression	Uses support vectors to perform regression with margin of tolerance
`m12_rnn`	Recurrent Neural Network	Neural network with feedback loops for sequential data
`m13_lstm`	Long Short-Term Memory	RNN variant designed to capture long-term dependencies
`m14_gru`	Gated Recurrent Unit	Simplified LSTM with fewer parameters
`m15_transformer`	Transformer	Attention-based model for sequence modeling without recurrence
`m16_prophet`	Prophet	Time series model developed by Facebook for business forecasting
`m17_xgb`	XGBoost	Gradient boosting framework optimized for speed and performance
`m18_nbeats`	N-BEATS	Deep learning model for univariate time series forecasting

Hyperparameter¶

The list of available model and its hyperparameter can be seen on config/model_hyperparameters.ipynb. The values currently available are the hyperparameter values mostly used in academic literature, but not necessarily the optimum value.