Forecasting of Electricity Consumption Rates
Challenge
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).
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).
Solution
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.
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.
Results
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%.
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%.