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Energy Data Analytics implies the broad utilization of data, statistical and quantitative analysis, explanatory and predictive models, and fact-based management to drive decisions and actions. Essentially, analytics is an arrangement of technology and processes that utilize data to comprehend and analyze the performance of an organization.

Regarding energy consumption and greenhouse gas emission by sector, the industrial sector consumes about 52% of the energy worldwide, implying that the industry is one of the main drivers of increasing electricity demand worldwide. With the flood of data available to businesses regarding their supply chain these days, companies are turning to energy data analytics solutions to extract meaning from the huge volumes of data to decrease their energy consumption. Organizations that are planning to optimize their sales and operations efforts require capacities to break down historical data, to predict the outcomes. The guarantee of doing it right and turning into a data-driven association is always appreciated. Immense economic benefits can be seen in any organization that has upgraded their supply network, brought down working costs, expanded incomes, or enhanced their client administration and some other activities.

As shown in the figure given below, individuals should consider the utilization of Energy Data Analytics in three stages.No one type of analytics is better than another, and in fact, they co-exist with and complement each other.

Description, Prediction, and Prescription

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1) Description

The descriptive analysis examines what is happening in real-time based on incoming data. Descriptive analysis is often referred to as the simplest type since it allows converting big data into useful bite-sized nuggets. Today, most large organizations don't stress considerably over the description stage since most likely they have just experienced. This stage is tied in with gathering data in databases which must be intended for the reason.

2) Prediction

The predictive analysis identifies past data patterns and provides a list of likely outcomes for a given situation. By studying recent and historical data, the predictive analysis presents you with a forecast of what may happen in the future. The prediction is viewed as a stage to grasp with considerably more pressing need. Predictive analysis can be very useful in optimizing customer relationship management.

3) Prescription

The relatively new field of prescriptive analytics allows users to “prescribe” a number of different possible actions to and guide them towards a solution. The prescriptive analysis reveals actions that should be taken and provides recommendations for next steps, letting you answer your business questions in a focused manner. In this stage, the part of human translators is particularly essential since they can reframe the complex outcomes from data analytics as noteworthy bits of knowledge that generalist managers can execute. In this stage, some of the complex mathematical methods are required including frontier benchmarking analysis utilizing SFA (Stochastic Frontier Analysis) or DEA (Data Envelopment Analysis) to distinguish what is best that can be expected to happen and stochastic optimization models to distinguish what activities are needed to be the best.

 

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