Data Science with Energy Management
The growth in hardware and sensor network in power systems has significantly increased the volume and velocity in energy transmission and distribution. The power system, therefore, has no choice to be a part of Big Data family where data science can take place. In this trend, managing energy data or energy management plays a crucial role, as discussed below.
In our world, energy sector is probably one of the first areas which raise criticism for our global warming issues. Hence, we are looking for some solution like using alternative energy resources or demand management. Investigating to renewable resources is expensive and time-consuming; while controlling demand could be more effective and simple.
At utility level, energy management can help the grid operators predict the future demand based on the consumer’s habit. Sometimes, extra generation and transmission capacity should be expanded to satisfy the peak (especially during World Cup or Olympic Games ….) even though the average daily consumption does not significantly change. As a consequence, the network can oversupply the loads during the day on which the demand curve is flat. The network operator can use management system to shift loads and hence lowering the operation cost while the average consumption remains.
At the consumer level, the insight from energy management can give the users feedback if they should turn on their appliances at a later moment to save more money in some places where the electricity price is time-varying.
In general, the modern energy management structure requires:
- Data (Real-time consumption, real-time generation, billing, events, weather, location (google maps)…..)
- End-users engagement
In summary, energy sector is probably one of the first areas which raise criticism for our global warming issues. It seems energy management can partially help us adjust our usage habit, which somehow releases some “climate change” tensions on the Earth (in a global picture).
By: Linh Viet Nguyen