No-Code AI to Predict Electricity Demand

Use Cases

Here's a hands-on example of how to predict electricity demand using AI.

Predicting electricity demand is needed to make decisions in power system planning and operation.

That said, gaining real value is about more than just building AI models. You need to implement those models and predictions in the real-world. What good is a predictive model if it’s never used? In this guide, we’ll explore how to deploy a custom machine learning model via Zapier, a no-code automation tool.

1. Building a electricity demand prediction model

To begin, simply make a Pro account on Obviously.AI — you get a 14-day free trial, with no credit card needed to sign up.

Once you log-in, you’ll see an “Add Dataset” icon on the left. For this example, we’ll upload a simple tabular dataset from Kaggle, which features 2,000 days of electricity demand data in Victoria, Australia.

Our goal is to predict a KPI called demand, which is the total daily electricity demand in MWh.

After clicking to upload a CSV file, you’ll need to verify the dataset, and simply check off that the first row is the column names and that all requirements are met.

Now, we select the column we want to predict, which is called “demand.” In the background, a series of machine learning models compete to create the most accurate predictions for electricity demand.

And we’re done! Now, we can also see how various attributes impact the electricity demand KPI. The main drivers discovered by the automated machine learning models were maximum temperature and solar exposure.

Let’s move on to building a Zapier sequence, so you can deploy predictions in the real world, and predict electricity demand.

2. Building a Zapier Flow

Now, make a free Zapier account, if you haven’t already. We’ll start by connecting Zapier to a dataset in Google Sheets. We’ll select “New Spreadsheet Row in Google Sheets” as the trigger to activate a Zapier sequence.

After selecting that trigger, I simply located my demo Google Sheet, which I titled “New Data.”

This sheet should have the exact same column names and data types as the file I used to make the predictive model (minus the actual target column of “demand”).

For instance, since my training data had columns like min_temperature, rainfall, and so on, I use those exact columns in this Google Sheet.

Next, we’ll select “Webhook,” and then “Custom Request.” This is how we’ll integrate with Obviously AI.

To set up the Custom Request, add in these details:

The data field is the most important, which requires matching up the column names in your data with the column names used in your model. Check the documentation if you’re unsure!

To set it all up, you’ll also need your Report ID and API key. Your API key can be found in your account, which gets added as a “Header” in Zapier. To get the Report ID, head to the “Export Predictions” tab in Obviously AI, and click on the shareable report link.

The Report ID is the ID you see in that URL. In the example above, it’s “cff97990–7385–11eb-9a76-af3704ed1fd7.” As you can see in the documentation, this gets added in the bottom of the “Data” field in Zapier.

Now, we can test our Zap, which sends data from our new data sheet to our electricity demand prediction model, and we’ll get the electricity demand probability as an output. With these predictions in Zapier, we can now do anything we want with them: Send them to a Slack channel, send an email to our team, add the prediction to our sheet, or anything else.

For now, let’s just update our sheet with the estimated electricity demand. This can be done with the action titled “Update Spreadsheet Row in Google Sheets.”

Then, simply add the probability created in the aforementioned webhook.

Now, let’s take it a step further. We can create a “filter” in Zapier to send an email notification when the predicted electricity demand is above a threshold that we set. I set up a filter that gets activated when the predicted electricity demand is higher than 146,000 MWh, which is a particularly high demand level. You can set this threshold to whatever makes sense in the context of your KPI.

If the predicted electricity demand is higher than 146,000 MWh, we can automatically send a notification email so that we’re aware of the situation.

Summary

Zapier is a powerful way to automate practically anything in your workflow. With Obviously AI, you can integrate the power of machine learning to create AI-driven workflows, including to predict electricity demand and make decisions in power system planning and operation.

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