All classes Electric Load Forecasting with LSTM Syllabus · Episode 2 of 6

LSTM Load Forecasting Class #2: Calendar Features, Temperature, and Lag; Time Series Windowing

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Time series feature engineering tutorial: calendar sine-cosine, holidays, temperature, lag, normalization, and windowing for LSTM. Episode 2 of the LSTM Electricity Load Forecasting series.

Software: Deep Learning (PyTorch LSTM + Algoritma Genetika)
Level: Intermediate
Duration: ± 10 min
Series total: 53 min · 6 videos
Published: October 25, 2026

Episode 2 of 6 · view syllabus

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Introduction

This is the final result, 11 features that translate time, holidays, and temperature into numbers that the neural network can understand, then arranged into tens of thousands of 168-hour windows to predict the next 24 hours. Let's start. Hello, welcome to episode two of the electric load forecasting series with LSTM. The model is only as good as the features it is given. In the previous episode, we saw that load depends on time, day, season, holidays, and temperature. Today, we are turning all of that into features, normalizing them properly, and arranging the data into windows for a time series model.

Step 1: Calendar, holidays, temperature & lag

Create a file named feature.py Time, day, and month are encoded with sine and cosine, because time is cyclical. Hour 23 should be close to hour 0, but as a regular number, they are far apart. Holidays use the holidays library for Belgium, combined with Saturdays and Sundays. Temperature is converted into heating degrees, which is how far the temperature is below 15 degrees. Lag feature 168 is the load at the same hour from one week prior. The first row without lag is discarded. The Scale class normalizes each feature using only the mean and standard deviation of the training data.

Step 2: Normalization & windowing

If statistics are calculated from all the data, information from the future leaks into training, making the test results appear better than they are. The prepare function calculates the features on the full data and records the training, validation, and test index boundaries. The window function takes the last 168 hours as input and the next 24 hours of load as the target. The history for the validation and test windows can be taken from the previous period, because in the real world, historical data is indeed available.

Step 3: Checking features & window

Create a file named check_underscore_feature.py and run it. There are 11 features, and about 31 percent of the hours are holidays or weekends. Normalized load in the training data has a mean of 0 and a standard deviation of 1. For training, the window is shifted by 1 hour, resulting in almost 26,000 examples shaped as 168 x 11. For validation, 1 forecast per day at every 00:00, exactly 365 windows.

Step 4: Features for one week

Let's look at the features for one week. The top row shows load and lag for 168 hours with a pattern that is very similar. The middle row shows sine and cosine of repeating hours smoothly every day. The bottom row shows temperature and weekend markers. With features like these, the model knows when a certain hour occurs and under what conditions.

Done!

Done. We have created calendar cycle, holiday, heating degree, and lag features, normalized without data leakage, and arranged the time series window. In the next episode, we create baseline, naive, ridge regression, and SARIMA. Are you working on a thesis, dissertation, or research in the field of load forecasting and time series? Cerdas Inovasi Digital helps with deep learning implementation, from load data exploration, feature engineering, ARIMA and regression, LSTM with PyTorch, up to hyperparameter optimization with genetic algorithms, as well as opening deep learning courses with PyTorch. Want to master the entire series, from hourly load data to forecast evaluation? All 6 episodes, complete with PDF step-by-step guides and source code, are available for premium tutorial subscribers. Subscribe starting at Rp69.000 per month, save 28% with the 12-month package, or pay once Rp999.000 for lifetime access. Want just one class? Buy a single class with lifetime access. Subscribe now at the premium page cerdasinovasidigital.com. Contact us via WhatsApp or visit cerdasinovasidigital.com. Thank you for watching, see you in the next episode. Thank you for watching,

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Transcript

0:03Introduction

0:46Step 1: Calendar, holidays, temperature & lag

3:02Step 2: Normalization & windowing

4:56Step 3: Checking features & window

6:39Step 4: Features for one week

8:12Done!

Ask the class material

My notes

Questions & answers

Your instructor

Recommended classes for you

Need more than a video?

Transcript

0:03Introduction

0:46Step 1: Calendar, holidays, temperature & lag

3:02Step 2: Normalization & windowing

4:56Step 3: Checking features & window

6:39Step 4: Features for one week

8:12Done!

Ask the class material

My notes

Questions & answers

Your instructor

Introduction

Step 1: Calendar, holidays, temperature & lag

Step 2: Normalization & windowing

Step 3: Checking features & window

Step 4: Features for one week

Done!

Introduction

Step 1: Calendar, holidays, temperature & lag

Step 2: Normalization & windowing

Step 3: Checking features & window

Step 4: Features for one week

Done!