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A Data-Driven Approach to Predict Hourly Load Profiles From Time-of-Use Electricity Bills

https://doi.org/10.1109/access.2023.3286020

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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Hourly energy profile determination technique from monthly energy bills

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Data-based method for creating electricity use load profiles using large amount of customer-specific hourly measured electricity use data

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A clustering approach to domestic electricity load profile characterisation using smart metering data

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Assessing the influence of the temporal resolution of electrical load and PV generation profiles on self-consumption and sizing of PV-battery systems

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Electric load shape benchmarking for small- and medium-sized commercial buildings

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Apples or oranges? Identification of fundamental load shape profiles for benchmarking buildings using a large and diverse dataset

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

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Building categorization revisited: A clustering-based approach to using smart meter data for building energy benchmarking

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A data-driven approach for electricity load profile prediction of new supermarkets

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A review of data mining technologies in building energy systems: Load prediction, pattern identification, fault detection and diagnosis

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A data-mining-based methodology to support MV electricity customers’ characterization

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Similarity measures and comparison methods for residential electricity load profiles

inferredcites →https://doi.org/10.1016/j.enbuild.2022.112753openalex-snapshot

A systematic review of building electricity use profile models

inferredcites →https://doi.org/10.1016/j.energy.2011.12.031openalex-snapshot

Overview and performance assessment of the clustering methods for electrical load pattern grouping

inferredcites →https://doi.org/10.1016/j.energy.2016.04.065openalex-snapshot

Classification of new electricity customers based on surveys and smart metering data

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The photovoltaic revolution is on: How it will change the electricity system in a lasting way

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Modeling technology retrofit scenarios for the conversion of condominium into an energy community: An Italian case study

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Renewable Energy Communities business models under the 2020 Italian regulation

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Performance evaluation of classification algorithms by k-fold and leave-one-out cross validation

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A review and an analysis of the residential electric load curve models

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Economic perspective for PV under new Italian regulatory framework

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Towards data-driven energy communities: A review of open-source datasets, models and tools

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Decarbonizing residential energy consumption under the Italian collective self-consumption regulation

inferredcites →https://doi.org/10.1016/j.segan.2019.100248openalex-snapshot

Comparing smart metered, residential power demand with standard load profiles

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Data-driven load profiles and the dynamics of residential electricity consumption

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An Introduction to Kernel and Nearest-Neighbor Nonparametric Regression

inferredcites →https://doi.org/10.1109/access.2021.3050074openalex-snapshot

A Comprehensive Review of Residential Electricity Load Profile Models

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Electrification, decarbonization, and the future carbon-free grid: the role of energy storage in the electric grid infrastructure [point of view]

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A Machine Learning Based Methodology for Load Profiles Clustering and Non-Residential Buildings Benchmarking

inferredcites →https://doi.org/10.1109/tpwrd.2011.2142198openalex-snapshot

Customer Classification and Load Profiling Method for Distribution Systems

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Smart Meter Driven Segmentation: What Your Consumption Says About You

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Predicting Consumer Load ProfilesUsing Commercial and Open Data

inferredcites →https://doi.org/10.1109/tsg.2018.2818167openalex-snapshot

Review of Smart Meter Data Analytics: Applications, Methodologies, and Challenges

inferredcites →https://doi.org/10.1109/tsg.2021.3078394openalex-snapshot

Conditional Multivariate Elliptical Copulas to Model Residential Load Profiles From Smart Meter Data

inferredcites →https://doi.org/10.1109/tsmc.1976.5408784openalex-snapshot

The Distance-Weighted k-Nearest-Neighbor Rule

inferredcites →https://doi.org/10.1198/tech.2007.s518openalex-snapshot

Pattern Recognition and Machine Learning

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Smart Metering Roll-Out in Europe: Where Do We Stand? Cost Benefit Analyses in the Clean Energy Package and Research Trends in the Green Deal

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Research on Electric Charge Calculation and Risk Control Strategy Based on Electricity Market Environment

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Ultra-short-term Photovoltaic Power Prediction Method Considering Historical Information Reuse

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IoT-based demand-side energy management: Enhancing peak hour efficiency through automated control of appliances

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Electricity Bill Prediction Based on a Particle Swarm Optimized Multilayer Perceptron Model

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Advances and Evaluation of Intelligent Techniques in Short-Term Load Forecasting

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