[HTML][HTML] Data analytics in the electricity sector–A quantitative and qualitative literature review

F vom Scheidt, H Medinová, N Ludwig, B Richter… - Energy and AI, 2020 - Elsevier
The rapid transformation of the electricity sector increases both the opportunities and the
need for Data Analytics. In recent years, various new methods and fields of application have …

Big data for internet of things: a survey

M Ge, H Bangui, B Buhnova - Future generation computer systems, 2018 - Elsevier
With the rapid development of the Internet of Things (IoT), Big Data technologies have
emerged as a critical data analytics tool to bring the knowledge within IoT infrastructures to …

A retrospective analysis of the impact of the COVID-19 restrictions on energy consumption at a disaggregated level

S García, A Parejo, E Personal, JI Guerrero, F Biscarri… - Applied energy, 2021 - Elsevier
Since the emergence of the virus that causes COVID-19 (the SARS-CoV-2) in Wuhan in
December 2019, societies all around the world have had to change their normal life patterns …

A short review on data mining techniques for electricity customers characterization

SS Cembranel, F Lezama, J Soares… - 2019 IEEE PES GTD …, 2019 - ieeexplore.ieee.org
An important tool to manage electrical systems is the knowledge of customers' consumption
patterns. Data Mining (DM) emerges as an important tool for extracting information about …

Recognition and classification of typical load profiles in buildings with non-intrusive learning approach

MS Piscitelli, S Brandi, A Capozzoli - Applied Energy, 2019 - Elsevier
The recent increasing spread of Advanced Metering Infrastructure (AMI) has enabled the
collection of a huge amount of building related-data which can be exploited by both energy …

Cross-temporal aggregation: Improving the forecast accuracy of hierarchical electricity consumption

E Spiliotis, F Petropoulos, N Kourentzes… - Applied Energy, 2020 - Elsevier
Achieving high accuracy in energy consumption forecasting is critical for improving energy
management and planning. However, this requires the selection of appropriate forecasting …

Extraction of statistical features for type-2 fuzzy NILM with IoT enabled control in a smart home

S Ghosh, A Chatterjee, D Chatterjee - Expert Systems with Applications, 2023 - Elsevier
Identification and monitoring of residential appliances are important facets for home energy
management and essential for proper functioning of the connected devices. In this paper …

Daily load forecasting based on a combination of classification and regression tree and deep belief network

PP Phyo, C Jeenanunta - IEEE Access, 2021 - ieeexplore.ieee.org
The next-day load forecasting is complex due to the load pattern variations driven by
external factors, such as weather and time. This study proposes a hybrid model that …

A pattern recognition methodology for analyzing residential customers load data and targeting demand response applications

A Rajabi, M Eskandari, MJ Ghadi, S Ghavidel, L Li… - Energy and …, 2019 - Elsevier
The availability of smart meter data allows defining innovative applications such as demand
response (DR) programs for households. However, the dimensionality of data imposes …

A cluster-based intelligence ensemble learning method for classification problems

S Cui, Y Wang, Y Yin, TCE Cheng, D Wang, M Zhai - Information Sciences, 2021 - Elsevier
Classification is a vital task in machine learning. By learning patterns of samples of known
categories, the model can develop the ability to distinguish the categories of samples of …