Data Science in the Energy Management Field

The energy sector is continuously developing, which gives a significant push to inventing new technologies and finding more way to advance the existing ones. It is also involved to a great extent in agriculture, transportation, manufacturing, telecommunication, and other industries. The demand in production increases energy consumption, which creates a need for developing technology that can monitor, collect, and analyze the quality and quantity of consumed energy. Data Science allows practitioners to evaluate the problems of energy management effectively, estimate its profit and maintenance cost by using a particular method for data analysis. However, to achieve the most effectiveness from implementing those methods, the field of energy management must apply strategies.

Data Science is focused on collecting information from different sources, organizing it, translating it into solutions, and estimating the most profitable way for its applying. Since the consumption of energy has significantly increased with the growth of population, the need for Data Science has appeared (Molina-Solana et al., 2017). The priority of this science is providing all the stakeholders in the field of energy strategy with effective strategy to profit. The effectiveness of Data Science in energy management is in the practical evaluation of affordable energy sources (Molina-Solana et al., 2017). However, it also involves improving the procedures and infrastructure of existing current energy management.

Data Science provides the evaluation of energy demand and consumption, analyzes the building and equipment maintains costs and estimates the patterns of energy consumption to detect errors. Moreover, it calculates the probability of a failure to minimize the cost of damage and repair. Data Science effectively predicts the dynamic of energy management, specifically its distribution, control, and communication (Molina-Solana et al., 2017). It involves using different techniques, which can help achieve more significant efficiency in energy management. Among many methods, the most popular are Classification, Regression, Clustering, association rules, Sequence discovery, Anomaly or Outlier detection, and Time series analysis (Molina-Solana et al., 2017).

The method of Classification is highly effective for energy management analysis (Molina-Solana et al., 2017). It develops an algorithm to divide new data into classes to specify decision boundaries (Molina-Solana et al., 2017). Regression technique evaluates the relationship between data, whether it is independent or dependant (Molina-Solana et al., 2017). Clustering separates variables into groups according to the similarity between them (Molina-Solana et al., 2017). The method of Association rules withdraws new information from raw data and, based on it, implicates the rules for a decision (Molina-Solana et al., 2017).

Sequence discovery seeks similar patterns n data and puts it in statistics (Molina-Solana et al., 2017). Anomaly or Outlier detection search for anomalies in patterns and unusual behaviour (Molina-Solana et al., 2017). The method of Time Series Analysis evaluates future data modules based on long-term recorded data (Molina-Solana et al., 2017). These techniques allow Data Science to predict the amount of energy required for specific time intervals or time instant.

It can estimate a peak demand with maximum efficiency, which helps in generating energy in higher need without any errors. It is also very useful in moderating and controlling processes and mechanisms in energy management. Moreover, Data Science provides an analysis that is crucial for energy efficiency and sustainability. In order to detect errors and prevent them, it verifies the operational status of the building and its maintenance to ensure the absence of energy fraud. However, the highest effectiveness of Data Science in energy management is in the economic analysis of energy consumption. It supplies the necessary data for evaluating the consumption rates and predicting the consuming behavior based on the data.

The article Data Science for Building Energy Management: a review by written by Molina-Solana, Ros, Ruiz, Gomez-Romero, and Martin-Bautista is reflecting the current effective use of Data Science techniques in energy management (2018). However, it does not provide practical strategies for implementing the Data Science methods into energy management (Amato & Venticinque, 2017). Moreover, the study should suggest the tools for the optimization of these applications in order to get the most useful predictions and analysis (Fauvel et al., 2018). Since technology changes very fast, there is a growing need to invent and implementing a new way of data analysis to provide stakeholders with high-quality service (Zhou et al., 2016). The energy industry is not an exception; moreover, the high demand for energy resources in the modern world creates a need for its improvement. Data Science can guarantee its growth if appropriately applied.

The importance of implementing the Data Science into Energy Management is undeniable. It can provide the energy industry with crucial data analysis to predict energy consumption, cost of technology and building maintenance. The technique it that are used in estimating information supply energy management with important tools for providing better service and receive stable profit at the same time. However, only Data Science is not enough for adequate evaluation of the received information, the strategy for successful implementation are needed as well. The combinations of all recourses will benefit the energy management the most significantly.


Amato A. & Venticinque S. (2017) Big Data for Effective Management of Smart Grids. In: Pedrycz W., Chen SM. (eds) Data Science and Big Data: An Environment of Computational Intelligence. Studies in Big Data, vol 24. Springer, Cham. Web.

Fauvel, C., Claveau, F., Chevrel, P., & Fiani, P. (2018). A flexible design methodology to solve energy management problems. International Journal of Electrical Power & Energy Systems, 97, 220–232. Web.

Molina-Solana, M., Ros, M., Ruiz, M. D., Gómez-Romero, J., & Martin-Bautista, M. (2017). Data science for building energy management: A review. Renewable and Sustainable Energy Reviews, 70, 598–609. Web.

Zhou, K., Fu, C., & Yang, S. (2016). Big data driven smart energy management: From big data to big insights. Renewable and Sustainable Energy Reviews, 56, 215–225. Web.

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