Big Data Analytics Framework for Renewable Energy Integration and Smart Grid Decision Support in Ghana

Abstract

The growing adoption of renewable energy sources, especially photovoltaic (PV) solar energy systems, introduces numerous difficulties for power systems due to generation intermittency, rising demands for electricity consumption, and the necessity for intelligent decision-making. In this regard, developing nations such as Ghana experience particular difficulties in deploying renewable energy sources that require sophisticated data management and analysis processes. Therefore, this article offers a conceptual Big Data analytics framework for the renewable energy sources integration and smart-grid decision-making in the context of the Ghana electricity industry. The suggested framework involves seven layers, which include data acquisition, data ingestion, distributed storage, distributed computing, analytics, visualization, and governance, among others, relying on such technologies as Apache Kafka, Hadoop Distributed File System (HDFS), Apache Cassandra, and Apache Spark. The proposed framework suggests that the machine learning algorithms such as Random Forest, Long Short-Term Memory (LSTM), and Isolation Forest can be applied to the future prediction of solar photovoltaic energy, future prediction of electricity demand and anomaly detection, respectively. A preliminary illustration using publicly available Ghanaian electricity demand data demonstrates the practical application context of the proposed framework; however, no machine learning models are implemented, trained, or experimentally validated in this study. The proposed framework provides a reference architecture to guide future implementation, empirical validation, and the development of data-driven smart-grid solutions for Ghana and other developing-country electricity systems.