Cases

Forecasting of Electricity Consumption Rates

AI, ML and Predictive Analytics

Cases

AI, ML and Predictive Analytics

Forecasting of Electricity Consumption Rates

Customer

Energy company Energy

Industry

Energy

Scale

10 000+

Challenge

Obtaining a short-term forecast of energy consumption for procurement planning on the Ukrainian Energy Exchange.

Solution

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

Result

Utilization of Recurrent neural networks (RNN) to build an effective ML-model. Customers can get short-term forecasts of energy consumption based on the analysis of historical data, with an accuracy of 96.4-99.5%.

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