Ashley Wright
Ashley Wright
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Neural Network Driven Insights into Electricity Consumption Data During COVID-19
The ongoing COVID-19 pandemic has caused widespread disruptions to everyday life and the global economy. Many existing forecast models no longer provide the same accuracy they once did. Consequently, being able to account for the change in conditions would prove quite valuable.
Oct 18, 2020
4 min read
Attributing Forecast Errors to COVID-19 Restrictions
There are a number of situations in which past data of the observation we are making forecasts for is not available. Possible reasons include time lags between data collection and data availability, and the time taken to quality control data.
Last updated on Oct 18, 2020
5 min read
Accounting for Model Drift
To improve forecasting capability during uncertain times it is necessary to both understand and address model drift. The hypothesis I aim to test is that electricity consumption forecasts will be more prone to errors in states that have seen more severe and prolonged restrictions as a result of COVID-19.
Last updated on Oct 18, 2020
4 min read
Developing Neural Networks to Forecast Electricity Consumption
Using the daily Victorian electricity consumption as an example, this blog will outline procedures to develop a forecasting model. To enable scheduling of energy supply for the next day I develop one day ahead forecasts.
Last updated on Oct 18, 2020
4 min read
Exploring Electricity Consumption Data
The COVID-19 pandemic has changed our understanding of uncertainty and what exactly normal is. We have seen economies free fall, toilet paper being hoarded, and unprecedented amounts of data made publicly available.
Last updated on Oct 18, 2020
4 min read
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