Introduction
Predicting peaks in different markets is useful. In the electricity market, consuming electricity at peak demand is penalized with higher tarifs. When an individual or company consumes electricity when its most demanded, regulators calls that a coincident peak (CP). In the Texas electricity market (ERCOT), the peak is the monthly 15-minute interval when the ERCOT Grid is at a point of highest capacity. The peak is caused by all consumers’ combined demand on the electrical grid. The coincident peak demand is an important factor used by ERCOT to determine final electricity consumption bills. ERCOT registers the CP demand of each client for 4 months, between June and September, and uses this to adjust electricity prices. Clients can therefore save on electricity bills by reducing the coincident peak demand. In this example we will train anMSTL
(Multiple Seasonal-Trend decomposition using LOESS) model on historic
load data to forecast day-ahead peaks on September 2022. Multiple
seasonality is traditionally present in low sampled electricity data.
Demand exhibits daily and weekly seasonality, with clear patterns for
specific hours of the day such as 6:00pm vs 3:00am or for specific days
such as Sunday vs Friday.
First, we will load ERCOT historic demand, then we will use the
StatsForecast.cross_validation
method to fit the MSTL model and forecast daily load during September.
Finally, we show how to use the forecasts to detect the coincident peak.
Outline
- Install libraries
- Load and explore the data
- Fit MSTL model and forecast
- Peak detection
Tip You can use Colab to run this Notebook interactively![]()
Libraries
We assume you have StatsForecast already installed. Check this guide for instructions on how to install StatsForecast. Install the necessary packages usingpip install statsforecast
Load Data
The input to StatsForecast is always a data frame in long format with three columns:unique_id
, ds
and y
:
-
The
unique_id
(string, int or category) represents an identifier for the series. -
The
ds
(datestamp or int) column should be either an integer indexing time or a datestamp ideally like YYYY-MM-DD for a date or YYYY-MM-DD HH:MM:SS for a timestamp. -
The
y
(numeric) represents the measurement we wish to forecast.
plot
method from the
StatsForecast
class. This method prints up to 8 random series from the dataset and is
useful for basic EDA.
Note TheStatsForecast.plot
method uses Plotly as a default engine. You can change to MatPlotLib by settingengine="matplotlib"
.

6,552
observations, so it is necessary to use
computationally efficient methods to deploy them in production.
Fit and Forecast MSTL model
The MSTL (Multiple Seasonal-Trend decomposition using LOESS) model decomposes the time series in multiple seasonalities using a Local Polynomial Regression (LOESS). Then it forecasts the trend using a custom non-seasonal model and each seasonality using a SeasonalNaive model.Tip Check our detailed explanation and tutorial on MSTL hereImport the
StatsForecast
class and the models you need.
[24, 24 * 7]
as the
seasonalities. See this
link for a
detailed explanation on how to set seasonal lengths. In this example we
use the
SklearnModel
with a LinearRegression
model for the trend component, however, any
StatsForecast model can be used. The complete list of models is
available here.
unique_id | ds | y | trend | |
---|---|---|---|---|
0 | ERCOT | 2021-01-01 00:00:00 | 43719.849616 | 1.0 |
1 | ERCOT | 2021-01-01 01:00:00 | 43321.050347 | 2.0 |
2 | ERCOT | 2021-01-01 02:00:00 | 43063.067063 | 3.0 |
3 | ERCOT | 2021-01-01 03:00:00 | 43090.059203 | 4.0 |
4 | ERCOT | 2021-01-01 04:00:00 | 43486.590073 | 5.0 |
StatsForecast
object with the following required parameters:
-
models
: a list of models. Select the models you want from models and import them. -
freq
: a string indicating the frequency of the data. (See panda’s available frequencies.)
Tip StatsForecast also supports this optional parameter.The
n_jobs
: n_jobs: int, number of jobs used in the parallel processing, use -1 for all cores. (Default: 1)fallback_model
: a model to be used if a model fails. (Default: none)
cross_validation
method allows the user to simulate multiple historic forecasts, greatly
simplifying pipelines by replacing for loops with fit
and predict
methods. This method re-trains the model and forecast each window. See
this
tutorial
for an animation of how the windows are defined.
Use the
cross_validation
method to produce all the daily forecasts for September. To produce
daily forecasts set the forecasting horizon h
as 24. In this example
we are simulating deploying the pipeline during September, so set the
number of windows as 30 (one for each day). Finally, set the step size
between windows as 24, to only produce one forecast per day.
unique_id | ds | cutoff | y | MSTL | |
---|---|---|---|---|---|
0 | ERCOT | 2022-09-01 00:00:00 | 2022-08-31 23:00:00 | 45482.471757 | 47413.944185 |
1 | ERCOT | 2022-09-01 01:00:00 | 2022-08-31 23:00:00 | 43602.658043 | 45237.153285 |
2 | ERCOT | 2022-09-01 02:00:00 | 2022-08-31 23:00:00 | 42284.817342 | 43816.390019 |
3 | ERCOT | 2022-09-01 03:00:00 | 2022-08-31 23:00:00 | 41663.156771 | 42972.956286 |
4 | ERCOT | 2022-09-01 04:00:00 | 2022-08-31 23:00:00 | 41710.621904 | 42909.899438 |
Important When usingcross_validation
make sure the forecasts are produced at the desired timestamps. Check thecutoff
column which specifices the last timestamp before the forecasting window.
Peak Detection
Finally, we use the forecasts incv_df
to detect the daily hourly
demand peaks. For each day, we set the detected peaks as the highest
forecasts. In this case, we want to predict one peak (npeaks
);
depending on your setting and goals, this parameter might change. For
example, the number of peaks can correspond to how many hours a battery
can be discharged to reduce demand.

Important In this example we only include September. However, MSTL can correctly predict the peaks for the 4 months of 2022. You can try this by increasing thenwindows
parameter ofcross_validation
or filtering theY_df
dataset. The complete run for all months take only 10 minutes.
Next steps
StatsForecast and MSTL in particular are good benchmarking models for peak detection. However, it might be useful to explore further and newer forecasting algorithms. We have seen particularly good results with the N-HiTS, a deep-learning model from Nixtla’s NeuralForecast library. Learn how to predict ERCOT demand peaks with our deep-learning N-HiTS model and the NeuralForecast library in this tutorial.References
- Bandara, Kasun & Hyndman, Rob & Bergmeir, Christoph. (2021). “MSTL: A Seasonal-Trend Decomposition Algorithm for Time Series with Multiple Seasonal Patterns”.
- Cristian Challu, Kin G. Olivares, Boris N. Oreshkin, Federico Garza, Max Mergenthaler-Canseco, Artur Dubrawski (2021). “N-HiTS: Neural Hierarchical Interpolation for Time Series Forecasting”. Accepted at AAAI 2023.