Since the invention of the steam-machine in the 17th century we have come a long way in our energy supply and usage. We have created worldwide energy networks that deliver electricity to 75% of the world. Due to big data we can now take the next step in the evolution of energy. Big data can turn existing old energy networks into smart networks that understand individual energy consumption.
Sensor-enabled products enable bi-directional communication between (energy) companies, smart meters and appliances in homes. When all appliances are connected to the Internet via sensors, the energy consumption of individual devices can be observed and regulated if required. More and more energy organizations are developing smart meters that already record consumption of electric energy in intervals. This information is returned to the energy company and it enables energy companies to understand and predict energy demand.
Predictability of usage is especially useful and important for the future of electric cars. Energy grids will not be able to cope with the peak in demand when consumers get home after work and plugin their electric cars all at the same time. The more devices that have sensors and can talk to the energy network, the better energy companies can manage the distribution of energy across its network.
Big Data, however, can also be used to predict maintenance. Vattenfall for example has installed sensor data within the wind turbines to predict when maintenance is needed. This will save the company a lot of fuel and money on unnecessary helicopter flights to the turbines and unnecessary maintenance as well as expensive consulting.
Data analytics may also be used to improve wind turbine placement for optimal energy output. The constantly changing weather data on a micro and macro level can help organizations predict the best spots for their wind turbines or solar systems depending on where, on an annual basis, the most wind or sun is predicted. Combined with structured and unstructured data such as tidal phases, geospatial and sensor data, satellite images, deforestation maps, and weather modeling it can help pinpoint the best place for installation.
Danish energy company Vestas Wind Systems for examples uses IBM big data analytics to analyze many different datasets to determine the best place for each individual wind turbine. Placing wind turbines at the wrong spot can result in not enough produced electricity to justify wind energy investments and increase electricity costs.
The most important effect of big data applied in the energy sector is that the existing networks are becoming much more efficient energy grids. This will reduce energy consumption and reduce prices for consumers. Smarter energy management can keep overloaded grids running and prevent the need for building new and expensive power plants. Less power plants that deliver energy more efficient and at lower prices could have a large affect on our carbon footprint.
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