{"id":135,"date":"2021-07-03T12:13:41","date_gmt":"2021-07-03T12:13:41","guid":{"rendered":"https:\/\/saveawatthour.com\/?p=135"},"modified":"2021-07-03T14:50:31","modified_gmt":"2021-07-03T14:50:31","slug":"what-is-smart-grid-data","status":"publish","type":"post","link":"https:\/\/saveawatthour.com\/index.php\/2021\/07\/03\/what-is-smart-grid-data\/","title":{"rendered":"Smart grid data and time"},"content":{"rendered":"\n

When talking about smart grid data it is hard to avoid the variable associated with time. A blackout occurred at a specific time<\/em>; high consumption will be expected between 1 and 4 pm<\/em> due to extreme heat leading to more usage of HVAC; customer Smith consumes less during the weekdays<\/em> than during the weekends <\/em>as he is working then; pricing is higher between 8 and 10 pm<\/em>; etc.<\/p>\n\n\n\n

As such most data (energy, pricing, weather, customer behavior) is recorded against time. This sort of data where data points occur in successive order over some period of time is called a time series<\/strong>.<\/p>\n\n\n\n

Time series allow us to track the evolution of data over time<\/strong>, we can see how the energy consumption varies with time, how weather relates to day-night patterns, or how customers unfold their specific daily or weekly habits. This aspect is most important as it allows utilities to plan for future events<\/strong> by analyzing the time series and applying machine learning techniques to predict consumption spikes for instances.<\/p>\n\n\n\n

The easiest way to represent time series data is through 2D plots<\/strong> where the data (vertical Y-axis) is plotted against time (horizontal X-axis) as you can see in the next image where the energy consumption (KWh) is plotted at 15-minute intervals (in this case between 16:30 and 19:00 on the 23rd of the month):<\/p>\n\n\n\n

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In a future post I will show how to load and prepare the raw data for analysis.<\/p>\n