Lightning charger load

The machine learning team is interested in predicting the energy consumed in a charger's next session, with the goal of routing power to the chargers who might be taxed in the future. To train a model, they'd like you to prepare a dataset that includes information about the user, vehicle model, charging station, and energy consumed that "looks" forward to the next charge. Time to show them what you've got!

This exercise is part of the course

Window Functions in Snowflake

Exercise instructions

Retrieve the time_of_day, charging_rate, and energy_consumed for each record from the ELECTRIC_VEHICLES.charging table.
Use a window function to "look ahead" and retrieve the energy consumed in the next charging session; return 0 if not found.
Segment the records by charging_station_id and use the charging_start_time to create a sequence of records, in ascending order.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

SELECT
    user_id,
    charging_station_id,
    charging_start_time,
    
    -- Retrieve the time_of_day, charging_rate and energy_consumed fields
	___,
    ___,
    ___,
	
    -- "Look ahead" to the energy consumed in the next session
    ___ OVER(
      
      	-- Segment the records by charging station and sequence
      	-- records by the start time of the charge
        ___
        ___
      
    ) AS next_session_energy_consumed

FROM ELECTRIC_VEHICLES.charging;

Edit and Run Code

This exercise is part of the course

Window Functions in Snowflake

IntermediateSkill Level

4.9+

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Open the window to a world of possibilities with Snowflake window functions! You'll get the ball rolling by differentiating window functions from traditional functions. Then, you'll learn how to provide a row number and ranking for each record in a query. Once you've nailed down the basics, you'll put the "window" in window functions, using PARTITION BY. You'll explore how to find and use the first and last value of a certain window before wrapping up with a sneak peek into aggregation functions.

Exercise 1: Assigning row numbers Exercise 2: Building a window function Exercise 3: Adding row numbers to charging sessions Exercise 4: Riding the heat wave Exercise 5: Ranking window functions Exercise 6: Was that a fast charge?Exercise 7: Comparing energy consumed Exercise 8: Partitioning data in a window function Exercise 9: Follow the money!Exercise 10: Digging into charging costs

Time to crank it up! In this chapter, you’ll take ranking functions to the next level. You’ll start with a variant of RANK, called DENSE_RANK, which handles ties in a bit of a different way. You’ll also explore a more robust version of the functions you saw in the previous lesson using NTH_VALUE. Next, you’ll create “buckets” of data using NTILE, which is more useful than you may think. You’ll also pick up a nifty little tool called CUME_DIST to find the number of records less than or equal to a certain record in a window. You’ll wrap up the chapter with one of the most powerful applications of window functions you’ve seen so far; LAG and LEAD.

Exercise 1: More Ranking Functions Exercise 2: Marketing the right vehicles Exercise 3: Optimizing charging scheduling Exercise 4: Setting a minimum charging cost Exercise 5: NTILE and CUME_DIST Exercise 6: Surveying vehicle owners Exercise 7: Evaluating charging duration Exercise 8: How long are vehicle owners going between charges?Exercise 9: LAG and LEAD Exercise 10: Looking into the past!Exercise 11: Maintaining charger efficiency Exercise 12: Lightning charger load

Current Exercise

You’ll start this final chapter with aggregation functions like AVG, COUNT, and SUM. You’ll compare the output of these functions to individual records in a window, as well as to perform additional calculations. After this, you’ll master the most exciting application of window functions; running and moving calculations! You’ll start by calculating running averages and totals for different metrics for electric vehicle charging. Finally, you’ll wrap up the course by generating moving totals and averages with a sliding window!

Exercise 1: Aggregate window functions Exercise 2: Engaging vehicle owners with metrics Exercise 3: Visualizing charging session duration and cost Exercise 4: Who's consuming the least?Exercise 5: Window frames Exercise 6: Are charging durations getting longer?Exercise 7: Identifying inefficient chargers Exercise 8: Moving Averages and Totals Exercise 9: Moving vs. running Exercise 10: Impact of temperature on charging rate Exercise 11: Putting it all together!Exercise 12: Congratulations!