On the public citation graph

Hybrid Deep Neural Network Model for Multi-Step Energy Prediction of Prosumers

0 signed · 65 inferred · view as graph

Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.

Is this your work? Sign in with ORCID and put your name on the claims that are yours.
Claim with ORCID
Reference health. See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.
See reference health

What this work says

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

inferredcites →https://doi.org/10.1007/s12559-020-09764-yopenalex-snapshot
A Novel Approach for Detecting Anomalous Energy Consumption Based on Micro-Moments and Deep Neural Networks
inferredcites →https://doi.org/10.1016/j.adapen.2021.100075openalex-snapshot
The economic potential of grid defection of energy prosumer households in Germany
inferredcites →https://doi.org/10.1016/j.aei.2021.101357openalex-snapshot
Forecasting building energy consumption: Adaptive long-short term memory neural networks driven by genetic algorithm
inferredcites →https://doi.org/10.1016/j.aei.2021.101442openalex-snapshot
Highly accurate energy consumption forecasting model based on parallel LSTM neural networks
inferredcites →https://doi.org/10.1016/j.apenergy.2014.02.057openalex-snapshot
Forecasting energy consumption of multi-family residential buildings using support vector regression: Investigating the impact of temporal and spatial monitoring granularity on performance accuracy
inferredcites →https://doi.org/10.1016/j.apenergy.2014.12.039openalex-snapshot
A clustering approach to domestic electricity load profile characterisation using smart metering data
inferredcites →https://doi.org/10.1016/j.apenergy.2017.12.054openalex-snapshot
Robust ensemble learning framework for day-ahead forecasting of household based energy consumption
inferredcites →https://doi.org/10.1016/j.apenergy.2019.114131openalex-snapshot
A hybrid model for building energy consumption forecasting using long short term memory networks
inferredcites →https://doi.org/10.1016/j.enbuild.2016.05.028openalex-snapshot
Time series forecasting for building energy consumption using weighted Support Vector Regression with differential evolution optimization technique
inferredcites →https://doi.org/10.1016/j.enbuild.2017.05.003openalex-snapshot
Day-ahead prediction of hourly electric demand in non-stationary operated commercial buildings: A clustering-based hybrid approach
inferredcites →https://doi.org/10.1016/j.enbuild.2019.109675openalex-snapshot
Study on deep reinforcement learning techniques for building energy consumption forecasting
inferredcites →https://doi.org/10.1016/j.enbuild.2020.110156openalex-snapshot
Deep learning and transfer learning models of energy consumption forecasting for a building with poor information data
inferredcites →https://doi.org/10.1016/j.enbuild.2021.110929openalex-snapshot
Hourly energy consumption prediction of an office building based on ensemble learning and energy consumption pattern classification
inferredcites →https://doi.org/10.1016/j.energy.2020.118530openalex-snapshot
Day-ahead prediction of hourly subentry energy consumption in the building sector using pattern recognition algorithms
inferredcites →https://doi.org/10.1016/j.energy.2021.121691openalex-snapshot
Data-driven energy prediction modeling for both energy efficiency and maintenance in smart manufacturing systems
inferredcites →https://doi.org/10.1016/j.engappai.2021.104440openalex-snapshot
Forecasting of individual electricity consumption using Optimized Gradient Boosting Regression with Modified Particle Swarm Optimization
inferredcites →https://doi.org/10.1016/j.esr.2020.100524openalex-snapshot
Hourly electricity demand forecasting using Fourier analysis with feedback
inferredcites →https://doi.org/10.1016/j.eswa.2017.09.059openalex-snapshot
Energy consumption forecasting based on Elman neural networks with evolutive optimization
inferredcites →https://doi.org/10.1016/j.ijepes.2020.106083openalex-snapshot
Forecasting hourly supply curves in the Italian Day-Ahead electricity market with a double-seasonal SARMAHX model
inferredcites →https://doi.org/10.1016/j.jobe.2021.103851openalex-snapshot
Robust building energy consumption forecasting using an online learning approach with R ranger
inferredcites →https://doi.org/10.1016/j.rser.2017.02.085openalex-snapshot
A review on time series forecasting techniques for building energy consumption
inferredcites →https://doi.org/10.1016/j.rser.2017.04.095openalex-snapshot
A review of data-driven building energy consumption prediction studies
inferredcites →https://doi.org/10.1016/j.rser.2020.110120openalex-snapshot
Data-driven predictive control for unlocking building energy flexibility: A review
inferredcites →https://doi.org/10.1016/j.rser.2020.110515openalex-snapshot
An overview of performance evaluation metrics for short-term statistical wind power forecasting
inferredcites →https://doi.org/10.1016/j.scs.2021.103445openalex-snapshot
An overview of machine learning applications for smart buildings
inferredcites →https://doi.org/10.1016/j.seta.2021.101085openalex-snapshot
Very short-term temperature forecaster using MLP and N-nearest stations for calculating key control parameters in solar photovoltaic generation
inferredcites →https://doi.org/10.1109/access.2019.2935273openalex-snapshot
Day-Ahead Hourly Forecasting of Solar Generation Based on Cluster Analysis and Ensemble Model
inferredcites →https://doi.org/10.1109/access.2019.2963045openalex-snapshot
Short-Term Prediction of Residential Power Energy Consumption via CNN and Multi-Layer Bi-Directional LSTM Networks
inferredcites →https://doi.org/10.1109/access.2020.2989316openalex-snapshot
An Innovative Optimization Strategy for Efficient Energy Management With Day-Ahead Demand Response Signal and Energy Consumption Forecasting in Smart Grid Using Artificial Neural Network
inferredcites →https://doi.org/10.1109/access.2021.3053069openalex-snapshot
A Pyramid-CNN Based Deep Learning Model for Power Load Forecasting of Similar-Profile Energy Customers Based on Clustering
inferredcites →https://doi.org/10.1109/access.2021.3056454openalex-snapshot
An Energy Management System Design Using Fuzzy Logic Control: Smoothing the Grid Power Profile of a Residential Electro-Thermal Microgrid
inferredcites →https://doi.org/10.1109/access.2021.3061370openalex-snapshot
Household-Level Energy Forecasting in Smart Buildings Using a Novel Hybrid Deep Learning Model
inferredcites →https://doi.org/10.1109/access.2021.3071654openalex-snapshot
Deep Learning-Based Short-Term Load Forecasting Approach in Smart Grid With Clustering and Consumption Pattern Recognition
inferredcites →https://doi.org/10.1109/access.2021.3093053openalex-snapshot
Short-Term Energy Forecasting Framework Using an Ensemble Deep Learning Approach
inferredcites →https://doi.org/10.1109/access.2021.3094089openalex-snapshot
Short-Term Energy Consumption Forecasting at the Edge: A Federated Learning Approach
inferredcites →https://doi.org/10.1109/access.2022.3140818openalex-snapshot
Household Energy Consumption Prediction Using the Stationary Wavelet Transform and Transformers
inferredcites →https://doi.org/10.1109/oajpe.2020.3029979openalex-snapshot
Energy Forecasting: A Review and Outlook
inferredcites →https://doi.org/10.1109/tia.2020.3049117openalex-snapshot
Day-Ahead Optimal Power Flow for Efficient Energy Management of Urban Microgrid
inferredcites →https://doi.org/10.1109/tsg.2013.2277171openalex-snapshot
Clustering-Based Improvement of Nonparametric Functional Time Series Forecasting: Application to Intra-Day Household-Level Load Curves
inferredcites →https://doi.org/10.1109/tste.2014.2313600openalex-snapshot
A Weather-Based Hybrid Method for 1-Day Ahead Hourly Forecasting of PV Power Output
inferredcites →https://doi.org/10.1109/tste.2017.2762435openalex-snapshot
Day-Ahead Hourly Forecasting of Power Generation From Photovoltaic Plants
inferredcites →https://doi.org/10.3390/app11052314openalex-snapshot
Study on Accuracy Metrics for Evaluating the Predictions of Damage Locations in Deep Piles Using Artificial Neural Networks with Acoustic Emission Data
inferredcites →https://doi.org/10.3390/app12010121openalex-snapshot
Future Renewable Energy Communities Based Flexible Power Systems
inferredcites →https://doi.org/10.3390/electronics11010022openalex-snapshot
Optimized Operation of Integrated Energy Microgrid with Energy Storage Based on Short-Term Load Forecasting
inferredcites →https://doi.org/10.3390/en13020494openalex-snapshot
Internet of Things (IoT) and the Energy Sector
inferredcites →https://doi.org/10.3390/en13102672openalex-snapshot
HousEEC: Day-Ahead Household Electrical Energy Consumption Forecasting Using Deep Learning
inferredcites →https://doi.org/10.3390/en15030833openalex-snapshot
Microgrid Operation Optimization Using Hybrid System Modeling and Switched Model Predictive Control
inferredcites →https://doi.org/10.3390/en15030863openalex-snapshot
Potential of Demand Response for Power Reallocation, a Literature Review
inferredcites →https://doi.org/10.3390/s22031051openalex-snapshot
Forecasting Day-Ahead Electricity Metrics with Artificial Neural Networks
inferredcites →https://doi.org/10.3390/su10040939openalex-snapshot
Transforming Data Centers in Active Thermal Energy Players in Nearby Neighborhoods
inferredcites →https://doi.org/10.3390/su12041417openalex-snapshot
Energy Flexibility Prediction for Data Center Engagement in Demand Response Programs

What points at this work

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

inferred← citeshttps://doi.org/10.2139/ssrn.4671146openalex-extracted
Fedwoa: A Federated Learning Model with the Whale Optimization Algorithm for Renewable Energy Prediction
inferred← citeshttps://doi.org/10.1007/978-3-032-10974-3_24openalex-snapshot
Prediction of Net Energy Demand for the Management of Transactive Energy Communities
inferred← citeshttps://doi.org/10.1016/j.compeleceng.2025.110259openalex-snapshot
A federated learning model with the whale optimization algorithm for renewable energy prediction
inferred← citeshttps://doi.org/10.1016/j.enconman.2024.118464openalex-snapshot
A novel scenario generation and forecasting method for multiple future states at source side based on combinatorial models
inferred← citeshttps://doi.org/10.1016/j.epsr.2023.109217openalex-snapshot
A local integrated electricity-heat market design among multi Smart Energy Hubs with renewable energy generation uncertainty
inferred← citeshttps://doi.org/10.1016/j.sasc.2026.200480openalex-snapshot
RS-ANFIS: A recurrent-state adaptive neuro-fuzzy model for accurate and interpretable Short-Term Load Forecasting in volatile energy systems
inferred← citeshttps://doi.org/10.1016/j.segan.2024.101522openalex-snapshot
Federated learning framework for prediction of net energy demand in transactive energy communities
inferred← citeshttps://doi.org/10.1109/fes65767.2025.11341807openalex-snapshot
Transformer Model with Fourier Embeddings for District Energy Prediction
inferred← citeshttps://doi.org/10.1109/jiot.2025.3549715openalex-snapshot
A Multistep Multivariate Fuzzy-Based Time-Series Forecasting on Internet of Things Data
inferred← citeshttps://doi.org/10.1109/jiot.2025.3564526openalex-snapshot
Block-FeST: Blockchain-Enhanced Federated Sparse Transformers for Privacy-Preserving RES Forecasting in Internet of Vehicles Systems
inferred← citeshttps://doi.org/10.2478/czoto-2023-0037openalex-snapshot
Analysis of the Required Energy Storage Capacity for Balancing the Load Schedule and Managing the Electric Energy Demand of an Apartment Building
inferred← citeshttps://doi.org/10.3390/buildings13061407openalex-snapshot
A Machine Learning Pipeline to Forecast the Electricity and Heat Consumption in a City District
inferred← citeshttps://doi.org/10.3390/math10234499openalex-snapshot
Blockchain-Based Distributed Federated Learning in Smart Grid
inferred← citeshttps://doi.org/10.3390/pr14050827openalex-snapshot
Special Issue on “Modeling, Simulation and Control of Energy Systems”
Editorial review for independent work. ICSAC — the Institute for Complexity Science and Advanced Computing — runs a structured editorial panel for researchers publishing outside journals, and publishes on CiteStamp rails. Submission is free and goes through ORCID: icsacinstitute.org/submit.