Utilizing Language Models for Energy Load Forecasting

Energy load forecasting plays a crucial role in optimizing resource allocation and managing energy consumption in buildings and cities. In this paper, we propose a novel approach that leverages language models for energy load forecasting. We employ prompting techniques to convert energy consumption data into descriptive sentences, enabling fine-tuning of language models. By adopting an autoregressive generating approach, our proposed method enables predictions of various horizons of future energy load consumption. Through extensive experiments on real-world datasets, we demonstrate the effectiveness and accuracy of our proposed method. Our results indicate that utilizing language models for energy load forecasting holds promise for enhancing energy efficiency and facilitating intelligent decision-making in energy systems.Comment: BuildSys 2023 Accepte

Human Mobility Question Answering (Vision Paper)

Question answering (QA) systems have attracted much attention from the artificial intelligence community as they can learn to answer questions based on the given knowledge source (e.g., images in visual question answering). However, the research into question answering systems with human mobility data remains unexplored. Mining human mobility data is crucial for various applications such as smart city planning, pandemic management, and personalised recommendation system. In this paper, we aim to tackle this gap and introduce a novel task, that is, human mobility question answering (MobQA). The aim of the task is to let the intelligent system learn from mobility data and answer related questions. This task presents a new paradigm change in mobility prediction research and further facilitates the research of human mobility recommendation systems. To better support this novel research topic, this vision paper also proposes an initial design of the dataset and a potential deep learning model framework for the introduced MobQA task. We hope that this paper will provide novel insights and open new directions in human mobility research and question answering research

Location Contact Tracing: Penetration, Privacy, Position and Performance

The recent COVID-19 pandemic changed radically the world and how people interact, move and behave. Following a lockdown that was imposed worldwide, although with different timing, Mobile Contact Tracing Apps (MCTA) were proposed to digitally trace contacts between individuals, while releasing gradually mobility constraints mandated to contain the disease spread. A general privacy concern on the use of GPS data shifted the efforts towards distributed applications, which use Bluetooth technology to trace proximity and potential infections. Nonetheless, GPS data would help more health operators to understand where hotbeds are, and to what extent the spread is progressing and at what pace. On top of these premises, in this work we take a closer look at the major pillars of MCTA, namely Penetration, Privacy, Position and Performance. We focus on (i) how the penetration rate affects the ability for a tracing applications to work, (ii) the proposal of a novel method of tracing, which build on the GPS technology, (iii) how the position of infections is beneficial to rapidly reduce the infection, and (iv) the discussion of the effects of such paradigm in different scenarios

MAPLE: Mobile App Prediction Leveraging Large Language model Embeddings

Despite the rapid advancement of mobile applications, predicting app usage remains a formidable challenge due to intricate user behaviours and ever-evolving contexts. To address these issues, this paper introduces the Mobile App Prediction Leveraging Large Language Model Embeddings (MAPLE) model. This innovative approach utilizes Large Language Models (LLMs) to predict app usage accurately. Rigorous testing on two public datasets highlights MAPLE's capability to decipher intricate patterns and comprehend user contexts. These robust results confirm MAPLE's versatility and resilience across various scenarios. While its primary design caters to app prediction, the outcomes also emphasize the broader applicability of LLMs in different domains. Through this research, we emphasize the potential of LLMs in app usage prediction and suggest their transformative capacity in modelling human behaviours across diverse fields