The project aims to transform energy management in Pakistan by analysing appliance-level energy consumption in six major cities: Karachi, Lahore, Islamabad, Peshawar, Skardu, and Multan. Each city represents a different climatic zone, providing a range of environmental conditions essential for understanding varied energy consumption patterns. By collecting detailed one-minute interval data, the project offers a precise view of household energy use and appliance efficiency, including air conditioners, refrigerators, and lighting systems. The initiative seeks to identify inefficiencies and patterns in energy consumption to develop strategies for reducing waste and improving efficiency. By analysing variations in energy use across different climates and times of day, the project aims to provide actionable insights for better appliance use, operational adjustments, and recommendations for more energy-efficient technologies. This data-driven approach is intended to influence policymaking, enhance energy management strategies, and contribute to national sustainability efforts. The findings are expected to advance energy conservation, reduce overall consumption, and support Pakistan’s goals for environmental sustainability and efficient resource management.

The LUMS Energy Institute (LEI) conducted the PRECON (Pakistan Residential Energy Consumption Dataset) project. This initiative is the first in Pakistan to collect extensive, appliance-level electricity consumption data from residential households. The project, conducted in 42 selected households in Lahore, provides highly detailed one-minute granularity data, especially for high-energy devices like air conditioners. This unique dataset is crucial for understanding residential energy demand and shaping future energy optimization efforts, including distributed solar energy adoption. The PRECON dataset not only benefits Pakistan but also has potential implications for energy policies in neighboring regions, such as Northern India, with similar climatic conditions. By offering this detailed and representative dataset, LEI aims to support informed decision-making in energy management, policy formulation, and sustainable energy solutions in Pakistan and the broader South Asian region.

In today's rapidly evolving world, large language models (LLMs) like ChatGPT, Google Bard, and Large Language Model Meta AI (LLaMA) have become powerful tools for knowledge extraction and generation through quick user prompts. However, a significant challenge with these models is their tendency to provide generic answers that lack domain-specific relevance. This limitation can hinder their effectiveness in specialized fields, such as energy consumption analysis, where precise insights are crucial for informed decision-making. This project aims to develop an Assistance Program for Large-Language Models (APLM) that enables these models to deliver domain-specific insights in the field of energy consumption. The primary objective is to enhance the practical applicability of ChatGPT by tailoring its responses to meet the unique needs of energy users, analysts, and decision-makers. The proposed model addresses this challenge by refining energy-related prompts, allowing ChatGPT to generate insights and recommendations that are both comprehensive and deeply rooted in the specifics of energy consumption. This approach ensures that the responses provided are not only accurate but also highly relevant to the field. By focusing on precise energy consumption patterns and relevant data, the APLM bridges the gap between generic responses and the detailed analysis required for effective energy management. This advancement will make LLMs more valuable tools for professionals in the energy sector, offering them the specific insights they need to make well-informed decisions. In conclusion, the APLM project seeks to improve the utility of LLMs in specialized fields, ensuring they can provide the precise, domain-specific information necessary for real-world applications, particularly in the critical area of energy consumption.

The Energy Informatics Lab at the Lahore University of Management Sciences (LUMS) is dedicated to merging data science, information technology, and energy systems to tackle pressing challenges in the energy sector. This innovative lab engages in several key research activities: Data Analysis and Modeling: Utilizing sophisticated data analytics and modeling techniques, the lab examines energy consumption patterns, optimizes energy utilization, and forecasts future energy demand. Renewable Energy Integration: Researchers explore how to seamlessly incorporate renewable energy sources into current energy infrastructures, aiming to enhance efficiency and sustainability. Energy Management Systems: The lab develops and assesses energy management systems that empower businesses and organizations to optimize their energy consumption and lower costs. Smart Grids and IoT: By focusing on smart grid technologies and Internet of Things (IoT) applications, the lab aims to improve the efficiency and reliability of energy distribution networks. Climate Change Mitigation: Research initiatives are geared toward creating strategies and technologies to reduce the impacts of climate change through enhanced energy efficiency and lower carbon emissions. Policy Development: The lab also conducts policy research to provide insights to government and industry stakeholders on effective practices and innovative energy solutions. Through collaboration with government agencies, industry partners, and academic institutions, the Energy Informatics Lab at LUMS plays a vital role in advancing energy informatics research, contributing to the transformation of the energy landscape in Pakistan and beyond.