Smart Grids Data Analysis: A Systematic Mapping Study

Abstract

Article under review. This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible.The article is structured as follows. In section II, we provide an overview of Smart Grids concepts: mainly architecture and components of the SG infrastructure. In section III, we define the goals, needs, process and research questions for the Systematic Mapping Study on data analysis in the SG context. In section IV, we present the results from the overall SMS process, divided into answers to the main research questions set. In Section V, we discuss the main threats to validity of the current study. Section VI provides the final conclusions.

Smart Grid Data Management in a Heterogeneous Environment with a Hybrid Load Forecasting Model

“…It also connects these electrical appliances to various sensors from where data are collected and sent to the distributors, who can then use the data for predictive analysis [22][23][24][25][26][27][28][29][30]. Smart homes are becoming increasingly popular in developed countries because they provide a high level of automation in controlling electrical appliances while ensuring that energy is not wasted by unnecessarily turning on the appliances [31]. Moreover, smart homes also allow users to track their energy usage at any time to monitor and control their energy bills.…”

“…Moreover, smart homes also allow users to track their energy usage at any time to monitor and control their energy bills. Therefore, smart homes make power consumption very efficient while reducing energy costs [24][25][26][27][28][29][30][31][32]. These existing and potential applications are highly effective in solving some of the energy problems, but there is still a need for more research to handle the energy problems that are more prevalent in developing countries.…”

“…The forecasting techniques can be classified into three main areas, namely long-term load forecasting (LTLF) for yearly observations, medium-term load forecasting (MTLF) for monthly observations, and short-term load forecasting (STLF) for daily or weekly observations. [30][31][32][33][34]. The suitable algorithms, techniques, and observed periods for load forecasting will fully depend on the forecast horizon type and the features of the data.…”

Big Data Platform for Smart Grids Power Consumption Anomaly Detection

“…There are a plethora of use cases for the application of big data analysis in the context of SGs [5], [6], like anomaly detection methods to detect power consumption anomalous behaviours [7], [8], the analysis of false data injection attacks [9], load forecasting for efficient energy management [10], among others. Such data analysis requirements create needs to define architectures and platforms to support large scale data analysis.…”

“…In this paper, we focus on power consumption data anomaly as the application scenario: dealing with the identification of anomalous patterns from energy consumption traces collected from smart meters, that can have several benefits for utilities, such as load optimizations based on determined patterns of energy usage [5], [7] or clustering of customers [11]. The final goal is the definition and evaluation of a big data platform for power consumption anomaly detection.…”