⚡ Recommendation ⚡

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💾 The data

The data is presented by the following columns:

• "date" - from January 1, 2015, to October 6, 2020.
• "demand" - daily electricity demand in MWh.
• "price" - recommended retail price in AUD/MWh.
• "demand_pos_price" - total daily demand at a positive price in MWh.
• "price_positive" - average positive price, weighted by the corresponding intraday demand in AUD/MWh.
• "demand_neg_price" - total daily demand at a negative price in MWh.
• "price_negative" - average negative price, weighted by the corresponding intraday demand in AUD/MWh.
• "frac_neg_price" - the fraction of the day when the demand traded at a negative price.
• "min_temperature" - minimum temperature during the day in Celsius.
• "max_temperature" - maximum temperature during the day in Celsius.
• "solar_exposure" - total daily sunlight energy in MJ/m^2.
• "rainfall" - daily rainfall in mm.
• "school_day" - "Y" if that day was a school day, "N" otherwise.
• "holiday" - "Y" if the day was a state or national holiday, "N" otherwise.

Note: The price was negative during some intraday intervals, so energy producers were paying buyers rather than vice-versa.

Missing data detected 🔎

In the columns 'solar_exposure' and 'rainfall', that would be filled by its median value.

🧮 Correlating The Data

Trust me, I'm a Data Scientist. ☝🏻
🔌🐽 And the first thing you always need to do is to check how the correlates your data in your dataset 🔎

The Price are correlates with the Demand and 🌡️ Maximal Temparature mainly.

💲 Price by month

As we see on the chart above: there is a pattern in the first month of the year, as well as that is the 🌡️ highest temperature month in Australia, what we can see in the frame below: