Climate and Energy Informatics Lab
Publications
Our lab focuses on advancing sustainable energy systems by integrating local community data, modeling power grids, and optimizing smart home technologies. We leverage real-time simulations, IoT devices, and advanced computing to create efficient and resilient energy solutions.
The energy sector is pivotal in driving the transition towards a sustainable net-zero future, with the adoption of digital technologies playing a key role in this evolution. While much of the existing research has centered on sustainability through centralized energy generation and large-scale transmission systems, this paper addresses a notable gap by focusing on how data-driven decisions in local power systems, supported by digitalization, can enhance efficiency, reliability, and sustainability. The framework of this paper is derived from a survey from eight organizations of power sector, covering transmission and distribution, aiming to answer the question: “Which decisions within local power systems need to be informed by data?”. Through inductive coding of survey responses, we identified key themes fall into two main categories: requirements of local energy systems and opportunities provided by digitalization to..
The decarbonisation of the built environment is a critical strategy for addressing climate change and decarbonisation of heat plays a significant role in this process. However, the UK’s progress in electrifying heating with heat pumps is significantly behind that of other European countries such as Finland and Norway. This study used clustering analysis to investigate UK consumers’ energy consumption patterns and their correlation with socioeconomic characteristics to identify suitable households for heat pumps. The study optimised K-means outlier removal (K-MOR) using genetic algorithms (GA) to reveal five typical energy consumption patterns consistent with a classification of residential neighbourhoods (ACORN) socioeconomic segments. The findings of our study indicate that the highest energy consumption pattern requires about 3 times the heating demand of the lowest pattern. Notably, 50.9% of households exhibit a middle-high load pattern, among which affluent households demonstrate higher heat pump adoption potential, while 14.44% of lower-income households face greater barriers to heat decarbonisation.
Distribution grids are evolving due to rising electricity demand and renewable energy integration, requiring efficient operation and effective planning. To achieve this, one essential step is translating the available load data into actionable insights. Machine learning (ML) approaches have emerged as promising solutions, leveraging increasing availability of data and computational capabilities. While research papers exist on applications of ML in power grids, a review in low-voltage substation-level is missing, an aspect that will be explored in this paper. The significance of emphasis at this level is twofold: ensuring privacy protection while gaining insights into consumption behavior, and eliminating the need for installing new meters or adjusting communication infrastructure. The paper covers three main ML algorithms, supervised, unsupervised, and reinforcement learning, their applications, while providing a critical..
Residential solar photovoltaic (PV) system installations are expected to continue increasing due to their growing cost competitiveness and supportive government policies. However, excessive installations of unknown behind-the-meter solar panels present a challenge for accurate load prediction and reliable operations of power networks. To address such growing concerns of distribution network operators (DNOs), this research proposes a novel model for distributed PV system capacity estimations. Innovative extracted features from 24-hour substation net load curves were fed into a deep neural network to estimate the PV capacity linked to the substation feeder. A comprehensive study into the sensitivity of the model’s accuracy to specific temporal scales of data collection, number of households served by a substation, and proportion of PV-equipped properties was conducted. This study revealed that a model …