Climate change and the resulting growing demand for renewable energy presents the energy sector with major challenges. Energy not only needs to be obtained from sustainable resources such as wind and sun; these resources also need to be exploited more efficiently. For although the volatile wind and sun technologies are able to cover a large proportion of requirements on windy and/or sunny days, on a grey, windless day the downtimes have to be compensated for with traditional energy sources such as fossil fuel.
Balance between offer and demand
Paradoxically, due to a lack of predictability, even on very sunny and windy days there is unnecessary energy consumption: Namely, if more energy is generated through solar and wind power systems than was forecast and the network operators are unable to react in time, traditional energy sources produce too much energy at the cost of the consumers - and thus also a great deal of superfluous CO2. One of the main challenges is to establish as optimal a balance as possible between supply and demand. And for precisely this purpose machine learning with the combination of two factors will play a key role: reliable prognoses for renewable energy sources and smart grids.
Reliable prognoses for renewable energy sources
Achieving predictability in power generation using wind turbines has led to major advances: While in Australia performance could be predicted to 80% accuracy over the course of the whole year, in the US in collaboration with the government, IBM has developed prognostics technology that can predict the sun and wind conditions for the next 15-30 days. Another representative example is the British AI company DeepMind, which can relatively precisely predict the performance of the turbines 36 hours in advance using weather forecasts and data from the wind turbines .
Smart grids complement reliable forecasts to achieve a better balance between supply and demand. Smart meters can generate data on energy consumption in terminal devices which are communicated via the smart grid infrastructure and can eventually lead to more efficient supplying. It may also be that power will be cheaper at off-peak times, so end consumers can reschedule flexible tasks that require a lot of energy.
Machine learning as part of the solution
There is enormous potential in general for machine learning and artificial intelligence to manage the problem of the balance between offer and demand in energy matters. Besides the two major areas of reliable forecasts and smart grids, many other aspects come into play for which great advances are to be expected, such as prevention of power theft or detection and prediction of power failures.
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