Big Data, Big Data Analytics, Data Mining and their applications in Industrial Engineering - Essay Example

The usage of predictive analytics in big data situation can help power plants forecast production size and also gain insight on prevailing challenges within the plant. Energy companies that are capable of managing and analysing high volume data by using advanced softwares like IBM information systems or any other suitable statistical software, can always transform their data into useful and profitable strategies. For instance, developing efficient demand response strategies requires that utilities apply innovative analytics across a mix of data about clients, consumption rate, generation capacity, physical grid unique behaviour, weather, and energy commodities market.

A Company’s decision backed by data are always more forward and acceptable as compared to using prevailing perceived information about the energy market. As mentioned before, power plants are welcoming the usage of smart grids and meters as a long term strategy that enables efficient energy supply. Energy companies can therefore create better power storage solutions, include uniform power generation points and in the process eliminate the need for new power plants. Essentially, this will enable power consumers to have more control over the amount of power/electricity they use.

A higher number of meter reads per day of power usage by consumers basically generate an overwhelming amount of data. This amount of data presents a great opportunity for power plants to study their consumers’ behaviours and also understand how market segmentation of customers can help industries know the amount of power output to achieve. For example, if you are an electricity producer, time of use pricing usually makes cost-savvy retail consumers to run dryers, dishwashers and washing machines at off-peak periods.

Evidently, these segment of consumers do not only save cash but they also need less generation capacity from their electricity providers.