Scalable data storage for PV monitoring systems

TL;DR

This paper introduces a scalable relational database system for PV monitoring data, utilizing compression and a decision-making algorithm to optimize storage and query performance for extensive experimental datasets.

Contribution

It presents a novel database architecture with optimized data compression and an input selection algorithm to enhance PV data management and analysis.

Findings

Reduced storage size through Timescaledb compression and Ramer-Douglas-Peucker algorithm.

Improved query performance for large PV datasets.

Effective selection of algorithm inputs for maximum data retention and space savings.

Abstract

Efficient PV research which includes a prolonged data monitoring from multiple experiments with different characteristics, requires a scalable supporting system to handle all of the collected information. This paper presents the development of a relational database for hosting all the necessary information for data modeling, comparative analysis and O\&M systems. Ramer-Douglas-Peucker algorithm and Timescaledb compression are used to decrease the size of the time-series data and increase the performance of the queries. A decision-making algorithm is presented for selecting the optimal inputs to the Ramer-Douglas-Peucker algorithm to ensure the maximum disk space savings while not losing any of the necessary information. Furthermore, alternative ways of implementing the same database are provided.