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Very Short-Term Load Forecaster Based on a Neural Network Technique for Smart Grid Control

https://doi.org/10.3390/en13195210

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What this work says

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

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Short-term load forecasting using a kernel-based support vector regression combination model

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Short-term residential load forecasting: Impact of calendar effects and forecast granularity

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Multi-model prediction and simulation of residential building energy in urban areas of Chongqing, South West China

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Short-term electricity load forecasting of buildings in microgrids

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Investigating the performance of support vector machine and artificial neural networks in predicting solar radiation on a tilted surface: Saudi Arabia case study

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A combined multivariate model for wind power prediction

inferredcites →https://doi.org/10.1016/j.energy.2018.04.072openalex-snapshot

Short-term power load probability density forecasting based on Yeo-Johnson transformation quantile regression and Gaussian kernel function

inferredcites →https://doi.org/10.1016/j.energy.2018.07.084openalex-snapshot

Potential of three variant machine-learning models for forecasting district level medium-term and long-term energy demand in smart grid environment

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Data-driven heating and cooling load predictions for non-residential buildings based on support vector machine regression and NARX Recurrent Neural Network: A comparative study on district scale

inferredcites →https://doi.org/10.1016/j.enpol.2018.04.060openalex-snapshot

Long term load forecasting accuracy in electric utility integrated resource planning

inferredcites →https://doi.org/10.1016/j.epsr.2015.09.001openalex-snapshot

Pattern-based local linear regression models for short-term load forecasting

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.ijforecast.2008.07.007openalex-snapshot

An evaluation of methods for very short-term load forecasting using minute-by-minute British data

inferredcites →https://doi.org/10.1016/j.neucom.2015.12.004openalex-snapshot

Forecasting electricity load with advanced wavelet neural networks

inferredcites →https://doi.org/10.1016/j.procs.2015.04.160openalex-snapshot

A Review of Short Term Load Forecasting using Artificial Neural Network Models

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Predicting solar energy generation through artificial neural networks using weather forecasts for microgrid control

inferredcites →https://doi.org/10.1016/j.rser.2012.02.044openalex-snapshot

Evaluation of hybrid forecasting approaches for wind speed and power generation time series

inferredcites →https://doi.org/10.1016/j.rser.2017.01.110openalex-snapshot

Statistic linear parametric techniques for residential electric energy demand forecasting. A review and an implementation to Chile

inferredcites →https://doi.org/10.1016/j.scs.2017.08.009openalex-snapshot

Electrical load forecasting models: A critical systematic review

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Utility companies strategy for short-term energy demand forecasting using machine learning based models

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Artificial neural networks in renewable energy systems applications: a review

inferredcites →https://doi.org/10.1109/epe.2017.7967255openalex-snapshot

Medium-term electric energy demand forecasting using Nadaraya-Watson estimator

inferredcites →https://doi.org/10.1109/epec.2017.8286222openalex-snapshot

Short-term load forecasting based on higher order partial least squares (HOPLS)

inferredcites →https://doi.org/10.1109/ijcnn.2009.5179034openalex-snapshot

Comparison of feedforward and feedback neural network architectures for short term wind speed prediction

inferredcites →https://doi.org/10.1109/isgteurope.2017.8260124openalex-snapshot

Medium-term electricity demand forecasting based on MARS

inferredcites →https://doi.org/10.1109/mepcon.2017.8301364openalex-snapshot

Short term load forecasting using ANN technique

inferredcites →https://doi.org/10.1109/pesgm.2014.6939012openalex-snapshot

Very short-term load forecasting based on NARX recurrent neural networks

inferredcites →https://doi.org/10.1109/pesgm.2017.8274209openalex-snapshot

Short-term load forecasting based automatic distribution network reconfiguration

inferredcites →https://doi.org/10.1109/ssci.2017.8285261openalex-snapshot

Short term load forecasting using multiple linear regression for big data

inferredcites →https://doi.org/10.1109/tla.2016.7437215openalex-snapshot

Efficient Neurofuzzy Model to Very Short-Term Load Forecasting

inferredcites →https://doi.org/10.1109/tpwrs.2012.2197639openalex-snapshot

Very Short-Term Load Forecasting: Wavelet Neural Networks With Data Pre-Filtering

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Electricity Load and Price Forecasting Using Jaya-Long Short Term Memory (JLSTM) in Smart Grids

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Microgrid-Level Energy Management Approach Based on Short-Term Forecasting of Wind Speed and Solar Irradiance

What points at this work

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

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Linking short-term electricity demand forecasting and explainable AI: A review for building energy applications

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Artificial intelligence-driven smart grid optimization: A comprehensive review of machine learning, renewable integration, and cybersecurity applications

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Ensemble forecaster based on the combination of time-frequency analysis and machine learning strategies for very short-term wind speed prediction

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Saturated load forecasting based on improved logistic regression and affinity propagation

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A novel methodology for day-ahead buildings energy demand forecasting to provide flexibility services in energy markets

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Intra-hour interval forecasting combined with concept drift adaptation for electric energy demand at residential level

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Very short-term parametric ambient temperature confidence interval forecasting to compute key control parameters for photovoltaic generators

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Ultra-Short- Term Load Forecasting Based on Convolutional-LSTM Hybrid Networks

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Enhancing Load Forecasting Accuracy in Smart Grids: A Novel Parallel Multichannel Network Approach Using 1D CNN and Bi‐LSTM Models

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Diseño optimizado de una máquina de reluctancia conmutada aplicada a un sistema de almacenamiento de energía para integración de energías renovables

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Stability Prediction in Smart Grid Using PSO Optimized XGBoost Algorithm with Dynamic Inertia Weight Updation

inferred← citeshttps://doi.org/10.3390/app11062538openalex-snapshot

A Very Short-Term Probabilistic Prediction Interval Forecaster for Reducing Load Uncertainty Level in Smart Grids

inferred← citeshttps://doi.org/10.3390/electricity7010021openalex-snapshot

A Comparative Study of Hybridized Machine Learning Models for Short-Term Load Prediction in Medium-Voltage Electricity Networks

inferred← citeshttps://doi.org/10.3390/en14123479openalex-snapshot

Chiller Load Forecasting Using Hyper-Gaussian Nets

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Time Series Forecasting for Energy Consumption

inferred← citeshttps://doi.org/10.37394/232016.2025.20.41openalex-snapshot

Adaptive Hybrid Electric Load Forecasting for Critical and Strategic Power Systems

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