Machine Learning for Legal Compliance in the Energy Sector - A Predictive Regulatory Framework

Aug 3, 2026

Increased complexity in energy regulations and sustainability standards creates a need for automated regulatory compliance monitoring. A predictive regulatory system is proposed that integrates legal compliance analysis with machine learning techniques for the energy sector. Using the Energy Efficiency dataset, Linear Regression, SVM, and Random Forest predict building energy loads and assess compliance with regulatory standards. Model robustness is evaluated with accuracy, precision, F1, and R², supported by statistical measures and feature correlation. The approach supports proactive detection of non-compliant cases and scalable, data-driven governance.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages24

Reading Time1 h 0 min

AccessFree to download

Abstract

Introduction

Digitalization and compliance challenges

EU AI Act and sector governance

Predictive compliance and literature frameworks

Machine Learning for Legal Compliance in the Energy Sector: A Predictive Regulatory Framework Enkeleda Olldashi1*, Elena Bebi2 Mostafa Abotaleb3 Hussein Alkattan4,5 Raed Hameed Chyad Alfilh 1 Department of Public Law, University of Tirana. Tirana, Albania. 2 Energy Department. Polytechnic University of Tirana, Tirana, Albania 3 Engineering School of Digital Technologies, Yugra State University, Khanty-Mansiysk, Russia 4 Department of System Programming, South Ural State University, Chelyabinsk, Russia 5 Directorate of Environment in Najaf, Ministry of Environment, Najaf, Iraq 6 Refrigeration & Air-Conditioning Technical Engineering Department, The Islamic University, Najaf, Iraq *[enkeleda.olldashi@fdut.edu.al](enkeleda.olldashi@fdut.edu.al) Abstract Increased complexity in energy regulations and sustainability standards has created a pressing need for automated regulatory compliance monitoring systems. A predictive regulatory system integrating legal compliance analysis with machine learning techniques in the energy sector is proposed in this work. On the Energy Efficiency dataset, Linear Regression, Support Vector Machines (SVM), and Random Forest were used to predict building energy loads and determine compliance with regulatory standards. The research demonstrates that machine learning enhances not just the precision of forecasts but also proactive identification of non-compliant cases, reducing legal vulnerabilities and helping policymakers implement standards of efficiency. Statistical measures and correlation determine the most impactful features, and relative performance metrics (accuracy, precision, F1, and R²) determine the robustness of the models. The system bridges the gap between energy engineering and regulation law and provides an energy sector compliance management solution that is scalable and data-driven. Keywords: Machine Learning; Legal Compliance; Energy Efficiency; Predictive Modelling; Random Forest; Support Vector Machines; Linear Regression; Regulatory Framework; Energy Sector; Compliance Monitoring INTRODUCTION The rapid digitalization of the energy sector is accompanied by daunting challenges of legal compliance, regulatory enforcement, and sustainability governance [1-7]. As digitalization, decentralization, and data intensification of the energy system accelerate, the role of artificial intelligence (AI) and machine learning (ML) in the operation of the energy sector has been identified as a strategic opportunity as much as a compliance necessity. Policy makers across the European Union and beyond are increasingly Engaged 643 Machine Learning for Legal Compliance in the Energy Sector: A Predictive Regulatory Framework in the creation of integrated legislation that addresses the ethical, legal, and technical issues surrounding AI within critical infrastructure like electricity, gas, and renewable energy networks [8-14]. The European Commission's Artificial Intelligence Act (AI Act) draft is a landmark regulatory measure to ensure the responsible deployment of AI within a range of sectors like the energy sector by risk categorization and the setting of transparent compliance regimes [1, 15-18]. The AI-energy nexus has been characterized by the International Energy Agency (IEA) as the "new power couple," with the possibilities of AI to improve renewable integration, boost grid optimization, and improve demand forecasts, and bring new regulatory and governance issues. Parallel industry observations are also noted in recent Capco initiatives, which determine the EU AI Act's application to the energy sector as aligning risk management, transparency, and reporting obligations [3]. These trends indicate the need for predictive compliance solutions that can correlate technical AI systems with legal enforcement mechanisms. To improve transparency andrigor in scoping reviews, systematic reviews and frameworks such as PRISMA-ScR have been utilized methodologically to a g

FAQs

What problem does the proposed framework address in the energy sector?

It addresses the growing complexity of energy regulations and sustainability standards by enabling automated, predictive compliance monitoring.

Which machine learning models are used for compliance-related predictions?

The work uses Linear Regression, Support Vector Machines (SVM), and Random Forest to predict building energy loads and determine regulatory compliance.

How is model performance and robustness evaluated?

Performance is assessed using accuracy, precision, F1 score, and R², along with statistical measures and correlation to identify impactful features.

Aug 3, 2026

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