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Distributed Power Load Missing Value Forecasting with Privacy Protection
In the era of the Internet of Things (IoT) supporting 5 G technology, the Smart Grid (SG) is an important part of Smart City. Specifically, load...
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Urban monthly power load forecasting based on economy-meteorology-gas demand coupling
Accurate power load forecasting is the key foundation and important premise of urban power system planning. Considering that urban power load demand...
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Short-term load forecasting based on empirical wavelet transform and random forest
Aiming at the problem of strong randomness and low forecasting accuracy in short-term electric load, a method based on empirical wavelet transform...
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Load Forecasting and Electricity Consumption by Regression Model
A non-standard method of forecasting load and power consumption using a regression model is considered. The training sample is taken as a basis,...
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Comparative Analysis of Load Forecasting by Using ANN, FUZZY Logic and ANFIS
With the paradigm shift of power transmission and distribution system towards decentralized control, there has been a great upsurge in forecasting of...
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Analysis of sports training and load forecasting using an improved artificial neural network
Artificial intelligence (AI) is being employed not only in businesses, entertainment, and academia but also in sports training to raise the level of...
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Combined Short-Term Load Forecasting Method Based on HHT
Short-term load forecasting of the power grid can realize the optimal configuration of power generation and dispatch of the power grid which saves...
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Time Load Forecasting: A Smarter Expertise Through Modern Methods
Electricity is a necessary aspect of modern life, and it benefits us in a variety of ways. Electricity is a part of daily living of human race, which...
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Evaluation of neural networks for residential load forecasting and the impact of systematic feature identification
Energy systems face challenges due to climate change, distributed energy resources, and political agenda, especially distribution system operators...
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Variable-Weighted Ensemble Forecasting of Short-Term Power Load Based on Factor Space Theory
The power load forecasting plays an important role in the economical and safe operation of the modern power system. However, the characteristics of...
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Enhanced neighborhood node graph neural networks for load forecasting in smart grid
Deep learning technology creates the condition for the optimization of the smart grid, and the big data analytical technique has the most efficient...
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A Decomposition-Based Improved Broad Learning System Model for Short-Term Load Forecasting
It is still a challenging problem for most existing forecasting methods to obtain accurate and rapid prediction performance in short-term load...
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Optimised extreme gradient boosting model for short term electric load demand forecasting of regional grid system
Load forecast provides effective and reliable guidance for power construction and grid operation. It is essential for the power utility to forecast...
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Levy flight-particle swarm optimization-assisted BiLSTM + dropout deep learning model for short-term load forecasting
This paper proposes a new optimized Deep Learning (DL) network design for time series load forecasting. At first, DL’s hyper parameters are optimized...
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Cloud Computing Load Forecasting by Using Bidirectional Long Short-Term Memory Neural Network
Cloud services play an increasingly significant role in daily life. The widespread integration of the Internet of Things, and online services has...
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Multi-Feature Integration Neural Network with Two-Stage Training for Short-Term Load Forecasting
Accurate short-term load forecasting (STLF) helps the power sector conduct generation and transmission efficiently, maintain stable grid operation...
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An innovative method-based CEEMDAN–IGWO–GRU hybrid algorithm for short-term load forecasting
The accuracy level of short-term load forecasting (STLF) affects the power department's arrangements for unit start-up, shutdown, overhaul, and load...
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A global forecasting method of heterogeneous household short-term load based on pre-trained autoencoder and deep-LSTM model
Short-term load forecasting (STLF) of heterogeneous multi-agents plays a significant role in smart grid. Faced with special difficulties of...
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Dual Attention Mechanism Based EMD-GRU for Electricity Load Forecasting
In order to fully exploit the correlation features of the data on power load time series and input data, and to increase the non-stationary power...
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IoT Based Load Forecasting for Reliable Integration of Renewable Energy Sources
Renewable energy resources have gathered substantial interest, and several nations are striving to use them as the dominant power resource. However,...
