Merge pull request #312 from josephatcatalysis/main

Updated Placement of blogs 2.2 and 2.3

website/blog-60daysofIA/2024-03-19/forecasting-energy-usage-with-intelligent-apps-2.md → website/blog-60daysofIA/2024-03-05/forecasting-energy-usage-with-intelligent-apps-2.md

@@ -1,5 +1,5 @@
 ---
-date: 2024-03-19T09:00
+date: 2024-03-05T09:05
 slug: forecasting-energy-usage-with-intelligent-apps-2
 title: "2.2 Forecasting Energy Usage with Intelligent Apps Part 2"
 authors: [cnteam]
@@ -30,7 +30,7 @@ tags: [Build-Intelligent-Apps, 60-days-of-IA, learn-live, hack-together, communi
 
 <!-- End METADATA -->
 
-![Graphic of a bar chart with a magnifying glass in front of it. To the left of the magnifying glass is a lightning bolt. At the bottom of the graphic is text that reads, "Forecasting Energy Usage with Intelligent Apps: Making Predictions."](../../static/img/60-days-of-ia/blogs/2024-03-19/2-2-1.png)
+![Graphic of a bar chart with a magnifying glass in front of it. To the left of the magnifying glass is a lightning bolt. At the bottom of the graphic is text that reads, "Forecasting Energy Usage with Intelligent Apps: Making Predictions."](../../static/img/60-days-of-ia/blogs/2024-03-05/2-2-1.png)
 
 *This three-part series demonstrates how to create an Intelligent App that forecasts future energy consumption and pricing based on historical data. In this second article, you’ll build out an app that analyzes historical data on energy consumption to build a forecasting model that forecasts future energy usage/pricing based on parameters input by the user.*
 
@@ -151,7 +151,7 @@ The output of the code snippet above should look like the following:
 
 ![Screenshot of output code in the terminal. It reads, "Fitting 5 folds for each of 6 candidates, totalling 30 fits
 Test RMSW: 5.738308690044228
-Test R2 Score: 0.8378464489048971](../../static/img/60-days-of-ia/blogs/2024-03-19/2-2-2.png)
+Test R2 Score: 0.8378464489048971](../../static/img/60-days-of-ia/blogs/2024-03-05/2-2-2.png)
 
 After fitting the model with cross-validation and hyperparameter tuning, you’ll see an average root mean squared error (RMSE) of approximately 5.74 and an R-squared (R2) score of about 0.84 on the test data. So, the R-squared values show promising performance in predicting energy prices!
 

website/blog-60daysofIA/2024-03-20/forecasting-energy-usage-with-intelligent-apps-3.md → website/blog-60daysofIA/2024-03-05/forecasting-energy-usage-with-intelligent-apps-3.md

@@ -1,5 +1,5 @@
 ---
-date: 2024-03-20T09:00
+date: 2024-03-05T09:10
 slug: forecasting-energy-usage-with-intelligent-apps-3
 title: "2.3 Forecasting Energy Usage with Intelligent Apps Part 3"
 authors: [cnteam]
@@ -30,7 +30,7 @@ tags: [Build-Intelligent-Apps, 60-days-of-IA, learn-live, hack-together, communi
 
 <!-- End METADATA -->
 
-![Graphic with a bar chart in a computer-like window in the top-right corner. To the left of the graph is a circle with a lightning bolt in it. At the bottom of the graphic is text that reads, "Forecasting Energy Usage with Intelligent Apps: Adding a Website Interface."](../../static/img/60-days-of-ia/blogs/2024-03-20/2-3-1.png)
+![Graphic with a bar chart in a computer-like window in the top-right corner. To the left of the graph is a circle with a lightning bolt in it. At the bottom of the graphic is text that reads, "Forecasting Energy Usage with Intelligent Apps: Adding a Website Interface."](../../static/img/60-days-of-ia/blogs/2024-03-05/2-3-1.png)
 
 *This three-part series demonstrates how to create an Intelligent App that forecasts future energy consumption and pricing based on historical data. In this final installment of the series, you’ll create a basic web interface that enables the user to input energy usage data and parameters, output the results and the model-generated report into the web interface for easy viewing. Finally, you’ll deploy using the AKS environment set up in Part 1. *
 
@@ -257,15 +257,15 @@ forecast-web-service   LoadBalancer   10.0.81.68   <EXTERNAL-IP>   80:30805/TCP
 
 Now, paste the `<External-IP>` into a new web browser tab to test your Forecast web app:
 
-![Screenshot of the Predicting Energy Pricing app open in a browser.](../../static/img/60-days-of-ia/blogs/2024-03-20/2-3-2.png)
+![Screenshot of the Predicting Energy Pricing app open in a browser.](../../static/img/60-days-of-ia/blogs/2024-03-05/2-3-2.png)
 
 Fill in the form with the energy fields, plus the date and time, and hit **Submit**.
 
 Once you submit the form, you’ll see predictions for energy prices categorized and a detailed report summarizing the electricity usage and price.
 
 Once the form is submitted, the Forecast web queries the model trained in Part 2 and obtains the forecast price. Then, it accesses the Forecast API service, which is hosted in your AKS cluster, to produce the summary report using the generative capabilities of the Llama2 Chat model:
 
-![Screenshot of the results in the Forecast  app. It includes an analysis of generation sources and their respective usage, a total for the actual load, and a price forecast.](../../static/img/60-days-of-ia/blogs/2024-03-20/2-3-3.png)
+![Screenshot of the results in the Forecast  app. It includes an analysis of generation sources and their respective usage, a total for the actual load, and a price forecast.](../../static/img/60-days-of-ia/blogs/2024-03-05/2-3-3.png)
 
 ### Why Build Intelligent Apps with KAITO?