相关论文: The Application of a Data Mining Framework to Ener…
Much effort is being made to ensure the safety of people. One of the main requirements of travellers and city administrators is to have knowledge of places that are more prone to criminal activities. To rate a place as a potential crime…
The information needed for fundamental modelling of the power markets -- the efficiency, start-up, fixed, and variable operating costs of each power plant -- is not publicly available. These parameters are usually estimated by considering…
Non-intrusive load monitoring (NILM) is the process of obtaining appliance-level data from a single metering point, measuring total electricity consumption of a household or a business. Appliance-level data can be directly used for demand…
Smart home technology is a better choice for the people to care about security, comfort and power saving as well. It is required to develop technologies that recognize the Activities of Daily Living (ADLs) of the residents at home and…
Understanding the heat usage of customers is crucial for effective district heating operations and management. Unfortunately, existing knowledge about customers and their heat load behaviors is quite scarce. Most previous studies are…
Web usage mining: automatic discovery of patterns in clickstreams and associated data collected or generated as a result of user interactions with one or more Web sites. This paper describes web usage mining for our college log files to…
Much research has been conducted on how consumption is related to human relations, for example, consumer communities organized around specific brands, or the way people use products to define their own identity and transmit a desired image.…
Cold load pick-up (CLPU) has been a critical concern to utilities. Researchers and industry practitioners have underlined the impact of CLPU on distribution system design and service restoration. The recent large-scale deployment of smart…
In this paper, we demonstrate that a consumer's marginal system impact is only determined by their demand profile rather than their demand level. Demand profile clustering is identical to cluster consumers according to their marginal…
Policy targets are being set increasingly for social and economic variables in the UK. This approach requires that reasonably successful ex ante forecasts can be made. We propose a general methodology for assessing the extent to which this…
Energy prediction in buildings plays a crucial role in effective energy management. Precise predictions are essential for achieving optimal energy consumption and distribution within the grid. This paper introduces a Long Short-Term Memory…
This paper proposes a method to track human figures in physical spaces and then utilizes this data to generate several data points such as footfall distribution, demographic analysis,heat maps as well as gender distribution. The proposed…
Buildings are significant contributors to global greenhouse gas emissions, accounting for 26% of global energy sector emissions in 2022. Meeting net zero goals requires a rapid reduction in building emissions, both directly from the…
With the increasing penetration of electric vehicles (EVs) into the automotive market, the electricity peak demand would increase significantly due to home-EV-charging. This paper tackles this problem by defining an 'ideal' EV consumption…
Widespread electric vehicle (EV) adoption introduces new challenges for distribution grids due to large, localized load increases, stochastic charging behavior, and limited data availability. This paper proposes two data-driven methods to…
Building Information Modeling has been used to analyze as well as increase the energy efficiency of the buildings. It has shown significant promise in existing buildings by deconstruction and retrofitting. Current cities which were built…
Energy systems modellers often resort to simplified system representations and deterministic model formulations (i.e., not considering uncertainty) to preserve computational tractability. However, reduced levels of detail and neglected…
Ensuring grid stability in the transition to renewable energy sources requires accurate power demand forecasting. This study addresses the need for precise forecasting by differentiating among industrial, commercial, and residential…
The performance of prediction models is often based on "abstract metrics" that estimate the model's ability to limit residual errors between the observed and predicted values. However, meaningful evaluation and selection of prediction…
In this paper we propose a Long Short-Term Memory Network based method to forecast the energy consumption in public buildings, based on past measurements. Our approach consists of three main steps: data processing step, training and…