Energy company Energy

Cases
Forecasting of Electricity Consumption Rates
AI, ML and Predictive Analytics
Cases
AI, ML and Predictive Analytics
Forecasting of Electricity Consumption Rates
Customer
Industry
Energy
Scale
10 000+
Obtaining a short-term forecast of energy consumption for procurement planning on the Ukrainian Energy Exchange.
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.
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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