Big Data Analytics for Renewable Energy Optimization (Solar, Wind, Smart Grid) - Zenodo 2025

Overview

The Big Data Analytics for Renewable Energy Optimization dataset published on Zenodo in November 2025 addresses the challenge of optimizing renewable energy systems through advanced data analytics and machine learning.

Data Sources

• Solar energy systems: Photovoltaic (PV) panel output, irradiance sensors, temperature measurements, inverter performance data, and weather station correlations.
• Wind energy systems: Turbine power output, wind speed, wind direction, blade pitch angles, yaw positions, generator temperatures, and gearbox vibrations.
• Smart grid infrastructure: Real-time grid demand, voltage levels, frequency stability, load distribution, and energy storage system (battery) charge/discharge cycles.

Dataset Characteristics

• High-volume time-series data spanning multiple months to years of continuous renewable energy generation and grid operation.
• Multi-site data collection from geographically distributed renewable energy installations enabling regional and system-level analysis.
• Integration of meteorological data (temperature, humidity, cloud cover, wind forecasts) for predictive modeling.
• Temporal granularity ranging from sub-minute (for grid stability analysis) to hourly and daily aggregates (for long-term forecasting).

Big Data Processing Framework

• Apache Hadoop: Distributed storage (HDFS) and batch processing (MapReduce) for large-scale historical data analysis.
• Apache Spark: In-memory distributed processing enabling real-time stream analytics and iterative machine learning on massive datasets.
• Data mining techniques: Pattern discovery, association rule mining, and clustering for identifying energy generation patterns and anomalies.
• Machine learning models: Regression (LSTM, ARIMA, Random Forest) for energy forecasting; classification for fault detection; reinforcement learning for grid optimization.

Optimization Objectives

• Energy forecasting: Predicting solar and wind generation hours to days ahead for grid planning and energy trading.
• Demand prediction: Forecasting electricity consumption patterns to balance supply and demand in real time.
• Efficiency enhancement: Identifying optimal operating parameters for turbines, inverters, and storage systems to maximize energy capture and minimize losses.
• Wastage reduction: Detecting curtailment scenarios (when renewable generation exceeds demand) and optimizing storage or load-shifting strategies.

Use Cases

• Grid operators: Improving integration of variable renewable energy sources into the grid with accurate forecasting and dynamic balancing.
• Energy utilities: Optimizing bidding strategies in electricity markets based on predicted generation and demand.
• Research institutions: Developing next-generation forecasting algorithms and exploring novel optimization techniques.
• Sustainability initiatives: Demonstrating scalable analytics-driven approaches to support renewable energy deployment and decarbonization goals.