Using Open Data on Energy: Applying the Scientific Method

This presentation explores how publicly available energy datasets can be used to conduct rigorous scientific research, uncover meaningful patterns in electricity consumption, and support the United Kingdom’s transition towards net-zero emissions.

Presented by Dinesh Chacko, the session demonstrates the practical application of the scientific method using Northern Powergrid’s Distribution Future Energy Scenarios (DFES) dataset. The research focuses on 2022 energy data from primary substations across North East England, Yorkshire, and northern Lincolnshire.

The presentation examines two research studies investigating relationships between energy consumption, renewable energy technologies, and electricity grid demand.

The first study explores domestic electricity consumption and its relationship with the adoption of heat pumps and electric vehicles (EVs). Through descriptive statistics, geographic visualisation, correlation analysis, and linear regression, the research identifies significant associations between domestic energy consumption and low-carbon technology adoption.

The second study investigates industrial electricity consumption, wind and solar generation capacity, and peak electricity demand at primary substations. The analysis explores whether renewable energy generation and industrial consumption influence peak demand, providing insights into the challenges of electricity distribution and network capacity planning.

Beyond the research findings, the presentation demonstrates a structured scientific approach, beginning with literature reviews and clearly defined research questions, followed by dataset selection, variable identification, analytical planning, statistical analysis, and interpretation of results.

Practical examples illustrate how tools such as Python, R, Excel, and interactive geographic mapping can transform open datasets into meaningful research evidence.

The presentation also highlights the importance of reproducibility, transparency, and interdisciplinary collaboration between engineering and data science.

Attendees will gain practical insights into applying scientific methods to open energy data, developing research questions, interpreting statistical relationships, and producing evidence-based findings suitable for academic publication and real-world energy planning.