Data Indexing in smart grid networks

Abstract

The transformation of conventional electrical networks into smart grids has led to the large-scale deployment of smart meters, sensors, and Internet of Things (IoT) devices. These technologies continuously generate massive volumes of operational data related to electricity consumption, environmental conditions, and grid behavior. Managing and analyzing this data efficiently has become a major challenge for modern energy systems. In particular, smart grid data are mainly represented as time-series data, where electricity consumption evolves over time and is influenced by multiple environmental and contextual factors. Traditional data management and indexing techniques are not well suited for handling such large, dynamic, and multidimensional datasets.

This thesis addresses the problem of efficient organization and retrieval of smart grid time-series data. The objective is to design indexing structures capable of supporting fast data retrieval, scalable storage, and real-time analysis while preserving the relationships between temporal patterns and electricity consumption behavior.

First, a statistical analysis of environmental and temporal variables influencing electricity consumption is conducted using Principal Component Analysis (PCA) and Analysis of Variance (ANOVA). This analysis identifies the most relevant features affecting electricity demand and provides a statistical foundation for constructing multidimensional indexing structures.

Second, a multidimensional indexing approach based on feature engineering and KD-Tree structures is proposed. Environmental variables are normalized, and temporal attributes are encoded using cyclic transformations to preserve their periodic nature. The resulting multidimensional representation allows observations to be organized in a unified feature space, enabling efficient similarity search and faster retrieval compared to traditional sequential scanning.

Third, a hybrid tree-based indexing model is introduced for large-scale smart grid time-series data. This structure integrates temporal segmentation and symbolic representation using Piecewise Aggregate Approximation (PAA) and iSAX concepts to reduce dimensionality while maintaining important consumption patterns. The hierarchical organization improves scalability and supports efficient search operations.

Finally, a new indexing structure called the Temporal Consumption Binary Tree (TCB-Tree) is proposed. This model organizes smart grid data simultaneously along temporal and consumption dimensions using horizontal and vertical segmentation strategies. The structure supports time-based, consumption-based, and hybrid queries within a unified framework, enabling efficient data retrieval in large smart grid datasets.

Experimental evaluations demonstrate that the proposed indexing approaches improve retrieval efficiency, scalability, and query performance when compared to traditional methods. These results highlight the importance of combining feature engineering, multidimensional representation, and specialized indexing structures for efficient smart grid data management.