Forecasting of Electricity Consumption Rates

Obtain a short-term energy consumption forecast for planning purchases on the Ukrainian Energy Exchange.

While analyzing Machine Learning algorithms and methods for short-term forecasting of events, we opted for using Recurrent neural networks (RNN). RNN made it possible to process sequential spatial chains or a series of events in time.
To conduct the research and set up the test RNNs, we leveraged the open-access hourly data stats on electricity consumption in New York City alongside temperature fluctuations during this period. As a result of our analysis, we managed to get an accurate forecast for two days’ consumption rates upfront by using a 3-month range of historical data.

Utilization of Recurrent neural networks (RNN) to build an effective ML-model. As a result, customers can get short-term forecasts of energy consumption based on the analysis of historical data, with a high accuracy.