相关论文: The Application of a Data Mining Framework to Ener…
We present an open digital ecosystem based on web-framework with a functional back-end server in user-centric energy retrofits. This data-driven web framework is proposed for building energy renovation benchmarking as part of an energy…
A smart home energy dataset that records miscellaneous energy consumption data is publicly offered. The proposed energy activity dataset (EAD) has a high data type diversity in contrast to existing load monitoring datasets. In EAD, a simple…
Energy flexibility, through short-term demand-side management (DSM) and energy storage technologies, is now seen as a major key to balancing the fluctuating supply in different energy grids with the energy demand of buildings. This is…
Customers' load profiles are critical resources to support data analytics applications in modern power systems. However, there are usually insufficient historical load profiles for data analysis, due to the collection cost and data privacy…
Localised data aggregation in many countries including Great Britain (GB) is typically done to a geographical level with polygon boundaries that have a robust and trusted governance system in place. At a minimum this will mean there is…
Frequent metering of electricity consumption is crucial for demand side management in smart grids. However, metered data can be processed fairly easily by employing well-established nonintrusive appliance load monitoring techniques to infer…
Accurate reporting of energy and carbon usage is essential for understanding the potential climate impacts of machine learning research. We introduce a framework that makes this easier by providing a simple interface for tracking realtime…
The growing demand for electrified heating, electrified transportation, and power-intensive data centres challenge distribution networks. If electrification projects are carried out without considering electrical distribution…
Power awareness is fast becoming immensely important in computing, ranging from the traditional High Performance Computing applications, to the new generation of data centric workloads. In this work we describe our efforts towards a power…
Energy usage monitoring on higher education campuses is an important step for providing satisfactory service, lowering costs and supporting the move to green energy. We present a collaboration between the Department of Statistics and…
Electricity consumed by residential consumers counts for a significant part of global electricity consumption and utility companies can collect high-resolution load data thanks to the widely deployed advanced metering infrastructure. There…
Mean profiles are widely used as indicators of the electricity consumption habits of customers. Currently, in \'Electricit\'e De France (EDF), class load profiles are estimated using point-wise mean function. Unfortunately, it is well known…
While non-parametric models, such as neural networks, are sufficient in the load forecasting, separate estimates of fixed and shiftable loads are beneficial to a wide range of applications such as distribution system operational planning,…
This paper exploits the Duration-of-Use of the demand patterns as a key concept for dealing with demand side flexibility. Starting from the consideration that fine-grained energy metering is not used at the point of supply of the…
Modelling Consumer Indebtedness has proven to be a problem of complex nature. In this work we utilise Data Mining techniques and methods to explore the multifaceted aspect of Consumer Indebtedness by examining the contribution of…
The increasing use of renewable energy sources with variable output, such as solar photovoltaic and wind power generation, calls for Smart Grids that effectively manage flexible loads and energy storage. The ability to forecast consumption…
Urban climate resilience requires more than high-resolution data; it demands systems that embed data collection, interpretation, and action within the daily lives of citizens. This chapter presents a scalable, citizen-centric framework that…
The environmental change and its effects, caused by human influences and natural ecological processes over the last decade, prove that it is now more prudent than ever to transition to more sustainable models of energy consumption…
This work presents ensemble forecasting of monthly electricity demand using pattern similarity-based forecasting methods (PSFMs). PSFMs applied in this study include $k$-nearest neighbor model, fuzzy neighborhood model, kernel regression…
Power management is an expensive and important issue for large computational infrastructures such as datacenters, large clusters, and computational grids. However, measuring energy consumption of scalable systems may be impractical due to…