Forecasting Energy Use
Problem being addressed
As the climate crisis deepens, energy grid management systems will play a key role in conserving and allocating energy resources. How can machine learning methods be used to forecast the load on the grid, and help to allocate energy in real-time?
A deep learning neural network architecture known as long short term memory deep learning (LTSM) is implemented to forecast the load on the energy grid. Two implementations are provided: standard LTSM and a LTSM sequence to sequence (S2S) architecture. In each implementation, the system is trained on data from an individual household power consumption data set that spans four years of measurement at one minute and one hour resolutions. The goal of the implementation was to forecast energy demand at both resolution levels. It was found that standard LTSM was less effective experimentally than the S2S approach; but overall the predictive accuracy of the model was very good.
Advantages of this solution
This is an important problem to solve, since energy management has the potential to save and conserve resources. Accurate load prediction models of great value to the energy sector.
Solution originally applied in these industries
Possible New Application of the Work
Electronics and Sensors Industry
IoT devices that talk to each other may also benefit from load management and anticipation systems. If devices communicate constantly, or at the same time, it may cause a systemic overload. Since many IoT devices are battery operated, this wastes resources.
Telecommunications networks also take strain under load. It would be interesting, but different, to predict the load on the telecommunications networks in the same way as the load on energy networks is done.
Transport and Logistics Industry
Transport networks move goods and people. This research could be extended to, for example, predict the load on public transport systems. Thus, allowing for congestion to be anticipated and mitigated.
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