Building load forecasting: Hospital in SF

We can train a forecaster on another commom energy problem. In this, case we are training a 1-step ahead forecaster to predict the electricity consumption of a building.

The dataset contains one year of hourly observations. The training will occur on 11 months of the data, reserving the last month for evaluation.

if "google.colab" in str(get_ipython()): # uninstall preinstalled packages from Colab to avoid conflicts !pip uninstall -y torch notebook notebook_shim tensorflow tensorflow-datasets prophet torchaudio torchdata torchtext torchvision !pip install git+https://github.com/ourownstory/neural_prophet.git # may take a while #!pip install neuralprophet # much faster, but may not have the latest upgrades/bugfixes import pandas as pd from neuralprophet import NeuralProphet, set_log_level set_log_level("ERROR") 

data_location = "https://raw.githubusercontent.com/ourownstory/neuralprophet-data/main/datasets/" sf_load_df = pd.read_csv(data_location + "energy/SF_hospital_load.csv") sf_load_df.head(3) 

	ds	y
0	2015-01-01 01:00:00	778.007969
1	2015-01-01 02:00:00	776.241750
2	2015-01-01 03:00:00	779.357338

Generic forecast: Time-based features only

In this first section, we will train a model with time-features only like we would do with Facebook Prophet.

m = NeuralProphet( weekly_seasonality=6, daily_seasonality=10, trend_reg=1, learning_rate=0.01, ) df_train, df_test = m.split_df(sf_load_df, freq="H", valid_p=1.0 / 12) metrics = m.fit(df_train, freq="H", validation_df=df_test, progress="bar") 

metrics.tail(1) 

	MAE_val	RMSE_val	Loss_val	RegLoss_val	epoch	MAE	RMSE	Loss	RegLoss
108	57.975113	80.128593	0.008979	0.0	108	46.005161	63.305355	0.004436	0.0

forecast = m.predict(df_train) m.set_plotting_backend("plotly-static") m.plot(forecast) 

forecast = m.predict(df_test) m = m.highlight_nth_step_ahead_of_each_forecast(1) m.plot(forecast[-7 * 24 :]) 

m.plot_parameters() 

1-step ahead forecast with Auto-Regression

m = NeuralProphet( growth="off", yearly_seasonality=False, weekly_seasonality=False, daily_seasonality=False, n_lags=3 * 24, ar_reg=1, learning_rate=0.01, ) df_train, df_test = m.split_df(sf_load_df, freq="H", valid_p=1.0 / 12) metrics = m.fit(df_train, freq="H", validation_df=df_test, progress="bar") 

metrics.tail(1) 

	MAE_val	RMSE_val	Loss_val	RegLoss_val	epoch	MAE	RMSE	Loss	RegLoss
108	24.246571	37.293633	0.002822	0.000877	108	23.822706	35.911766	0.002339	0.000877

forecast = m.predict(df_train) m.set_plotting_backend("plotly-static") m.plot(forecast) 

forecast = m.predict(df_test) m = m.highlight_nth_step_ahead_of_each_forecast(1) m.plot(forecast[-7 * 24 :]) 

m.plot_parameters() 

1 step ahead forecast with AR-Net: Using a Neural Network

Here, we will use the power of a neural Network to fit non-linear patterns.

m = NeuralProphet( growth="off", yearly_seasonality=False, weekly_seasonality=False, daily_seasonality=False, n_lags=3 * 24, ar_layers=[32, 32, 32, 32], learning_rate=0.003, ) df_train, df_test = m.split_df(sf_load_df, freq="H", valid_p=1.0 / 12) metrics = m.fit(df_train, freq="H", validation_df=df_test, progress="bar") 

metrics.tail(1) 

	MAE_val	RMSE_val	Loss_val	RegLoss_val	epoch	MAE	RMSE	Loss	RegLoss
108	7.14312	10.696775	0.00016	0.0	108	6.197587	9.060362	0.000093	0.0

forecast = m.predict(df_train) m.set_plotting_backend("plotly-static") m.plot(forecast) 

forecast = m.predict(df_test) m = m.highlight_nth_step_ahead_of_each_forecast(1) m.plot(forecast[-7 * 24 :]) 

m.plot_components(forecast[-7 * 24 :])