All classes Electric Load Forecasting with LSTM Syllabus · Episode 3 of 6

Class LSTM Electricity Load Forecasting #3: Baseline: Weekly Naive, Ridge Regression, and SARIMA

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Electricity load forecasting baseline tutorial: naive, Ridge regression, and SARIMA with statsmodels, measured using MAPE and RMSE. Episode 3 of the LSTM electricity load forecasting series.

Software: Deep Learning (PyTorch LSTM + Algoritma Genetika)
Level: Intermediate
Duration: ± 7 min
Series total: 53 min · 6 videos
Published: October 25, 2026

Episode 3 of 6 · view syllabus

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Introduction

Here is the final result, three ways to forecast 24 hours ahead, from the simplest to the classical statistical Sarima model. The results are surprising, Sarima actually lost to the naive guess, and simple linear regression became the target that LSTM needs to beat. Let's start. Hello, welcome to episode three of the electricity load forecasting series with LSTM. Before building a complex model, good research always has a baseline. Without a baseline, we don't know whether LSTM is truly good or just looks good. Today we are making three baselines and two metrics, map, and galak percentage absolute average, and RMSI in megawatts.

Step 1: Metrics, Naive, and Ridge Regression

Create the baseline.py file. The map function calculates the average galak in percent, and RMSI is the root mean square of galak in megawatts. We prepare the training window and test window for 2019, one forecast per day. Baseline one, weekly naive, the forecast for tomorrow is the same as the load at the same hour one week ago, that is, the first 24 hours of the window. Baseline two, ridge regression, the input is the last 168 loads plus the last hour feature, and the output is 24 hours at once. Baseline three, Sarima 201 with seasonal period 111, 24, a classical statistical model for seasonal time series.

Step 2: SARIMA

The model is trained on the last three months of training data. For each test day, the model is updated with the last 14 days without retraining, then forecasts 24 hours. This method is much faster than retraining 365 times. Now the tables and graphs.

Step 3: Baseline Comparison

Run it, Sarima takes about half a minute. Weekly naive is already quite good, map around 4.4%. Ridge regression is the best, around 3.3%. Sarima actually performs the worst, around 5.6%, because it only models the daily pattern, and doesn't know the difference between weekdays and weekends. The graph shows one week in February, Sarima deviates at the weekend. LSTM's target is to beat this 3.3%.

Done!

Done. We have created three baselines, weekly naive, ridge regression, and Sarima, and measured them with map and RMSI. In the next episode, we build an LSTM to forecast the next 24 hours. Currently working on a thesis, dissertation, or research in the field of electrical load and time series forecasting? Cerdas Inovasi Digital helps with deep learning implementation, from 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 six episodes, complete with step-by-step PDF guides and source code, are available to 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.

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Transcript

0:03Introduction

0:51Step 1: Metrics, Naive, and Ridge Regression

3:19Step 2: SARIMA

4:01Step 3: Baseline Comparison

5:37Done!

Ask the class material

My notes

Questions & answers

Your instructor

Recommended classes for you

Need more than a video?

Transcript

0:03Introduction

0:51Step 1: Metrics, Naive, and Ridge Regression

3:19Step 2: SARIMA

4:01Step 3: Baseline Comparison

5:37Done!

Ask the class material

My notes

Questions & answers

Your instructor

Introduction

Step 1: Metrics, Naive, and Ridge Regression

Step 2: SARIMA

Step 3: Baseline Comparison

Done!

Introduction

Step 1: Metrics, Naive, and Ridge Regression

Step 2: SARIMA

Step 3: Baseline Comparison

Done!