Electrical load ≈ Electrical load
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Short-Term Load Forecasting Using EMD-LSTM Neural Networks with a Xgboost Algorithm for Feature Importance Evaluation Open
Accurate load forecasting is an important issue for the reliable and efficient operation of a power system. This study presents a hybrid algorithm that combines similar days (SD) selection, empirical mode decomposition (EMD), and long shor…
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Hybrid CNN-LSTM Model for Short-Term Individual Household Load Forecasting Open
Power grids are transforming into flexible, smart, and cooperative systems with greater dissemination of distributed energy resources, advanced metering infrastructure, and advanced communication technologies. Short-term electric load fore…
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A Short-Term Load Forecasting Method Using Integrated CNN and LSTM Network Open
In this study, a new technique is proposed to forecast short-term electrical load. Load forecasting is an integral part of power system planning and operation. Precise forecasting of load is essential for unit commitment, capacity planning…
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A Deep Neural Network Model for Short-Term Load Forecast Based on Long Short-Term Memory Network and Convolutional Neural Network Open
Accurate electrical load forecasting is of great significance to help power companies in better scheduling and efficient management. Since high levels of uncertainties exist in the load time series, it is a challenging task to make accurat…
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A Comprehensive Review of the Load Forecasting Techniques Using Single and Hybrid Predictive Models Open
Load forecasting is a pivotal part of the power utility companies. To provide load-shedding free and uninterrupted power to the consumer, decision-makers in the utility sector must forecast the future demand for electricity with a minimum …
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Short-Term Load Forecasts Using LSTM Networks Open
With the increasing load requirements and the sophistication of power stations, knowing in advance about the electrical load not only at short-term periods such as hours or couple of days but also over the longer-term periods such as weeks…
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A High Precision Artificial Neural Networks Model for Short-Term Energy Load Forecasting Open
One of the most important research topics in smart grid technology is load forecasting, because accuracy of load forecasting highly influences reliability of the smart grid systems. In the past, load forecasting was obtained by traditional…
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Electrical Load Forecasting Using LSTM, GRU, and RNN Algorithms Open
Forecasting the electrical load is essential in power system design and growth. It is critical from both a technical and a financial standpoint as it improves the power system performance, reliability, safety, and stability as well as lowe…
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On Short-Term Load Forecasting Using Machine Learning Techniques and a Novel Parallel Deep LSTM-CNN Approach Open
Since electricity plays a crucial role in countries' industrial infrastructures, power companies are trying to monitor and control infrastructures to improve energy management and scheduling. Accurate forecasting is a critical task for a s…
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Load Forecasting Techniques for Power System: Research Challenges and Survey Open
The main and pivot part of electric companies is the load forecasting. Decision-makers and think tank of power sectors should forecast the future need of electricity with large accuracy and small error to give uninterrupted and free of loa…
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Methods and Models for Electric Load Forecasting: A Comprehensive Review Open
Electric load forecasting (ELF) is a vital process in the planning of the electricity industry and plays a crucial role in electric capacity scheduling and power systems management and, therefore, it has attracted increasing academic inter…
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Short term electricity load forecasting using hybrid prophet-LSTM model optimized by BPNN Open
Electrical load forecasting plays a vital role in the operation and planning of power plants for the utility companies and policy makers to design stable and reliable energy infrastructure. Load forecasting is categorized in long-term, mid…
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Electric Vehicle Charging Load Forecasting: A Comparative Study of Deep Learning Approaches Open
Load forecasting is one of the major challenges of power system operation and is crucial to the effective scheduling for economic dispatch at multiple time scales. Numerous load forecasting methods have been proposed for household and comm…
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A Deep Learning Method for Short-Term Residential Load Forecasting in Smart Grid Open
Residential demand response is vital for the efficiency of power system. It has attracted much attention from both academic and industry in recent years. Accurate short-term load forecasting is a fundamental task for demand response. While…
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Short-Term Electric Load Forecasting Using Echo State Networks and PCA Decomposition Open
In this paper we approach the problem of forecasting a time-series of electrical load measured on the ACEA power grid, the company managing the electricity distribution in the city of Rome – Italy, with an Echo State Network considering tw…
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Deep-Learning Forecasting Method for Electric Power Load via Attention-Based Encoder-Decoder with Bayesian Optimization Open
Short-term electrical load forecasting plays an important role in the safety, stability, and sustainability of the power production and scheduling process. An accurate prediction of power load can provide a reliable decision for power syst…
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A Review on Plug-in Electric Vehicles: Introduction, Current Status, and Load Modeling Techniques Open
Plug-in electric vehicle (PEV) load modeling is very important in the operation and planning studies of modern power system nowadays. Several parameters and considerations should be taken into account in PEV load modeling, making it a comp…
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Enhanced Deep Networks for Short-Term and Medium-Term Load Forecasting Open
Electric load forecasting (ELF) is vitally beneficial for electrical power planning and economical running in smart grid. However, the medium-term load forecasting has been rarely studied. In addition, existing ELF models mainly consider t…
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Short-Term Forecasting of Electric Loads Using Nonlinear Autoregressive Artificial Neural Networks with Exogenous Vector Inputs Open
Short-term load forecasting is crucial for the operations planning of an electrical grid. Forecasting the next 24 h of electrical load in a grid allows operators to plan and optimize their resources. The purpose of this study is to develop…
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Short-Term Load Forecasting with Multi-Source Data Using Gated Recurrent Unit Neural Networks Open
Short-term load forecasting is an important task for the planning and reliable operation of power grids. High-accuracy forecasting for individual customers helps to make arrangements for generation and reduce electricity costs. Artificial …
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Modelling electric and heat load profiles of non-residential buildings for use in long-term aggregate load forecasts Open
Long-term forecasts of the aggregate electric load profile are crucial for grid investment decisions and energy system planning. With current developments in energy efficiency of new and renovated buildings, and the coupling of heating and…
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Electric Load Clustering in Smart Grid: Methodologies, Applications, and Future Trends Open
With the increasingly widespread of advanced metering infrastructure, electric load clustering is becoming more essential for its great potential in analytics of consumers' energy consumption patterns and preference through data mining. Mo…
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Forecasting of Electric Load Using a Hybrid LSTM-Neural Prophet Model Open
Load forecasting (LF) is an essential factor in power system management. LF helps the utility maximize the utilization of power-generating plants and schedule them both reliably and economically. In this paper, a novel and hybrid forecasti…
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Probabilistic Load Forecasting Based on Adaptive Online Learning Open
Load forecasting is crucial for multiple energy management tasks such as\nscheduling generation capacity, planning supply and demand, and minimizing\nenergy trade costs. Such relevance has increased even more in recent years due\nto the in…
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Short-Term Electric Load and Price Forecasting Using Enhanced Extreme Learning Machine Optimization in Smart Grids Open
A Smart Grid (SG) is a modernized grid to provide efficient, reliable and economic energy to the consumers. Energy is the most important resource in the world. An efficient energy distribution is required as smart devices are increasing dr…
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A Short-Term Residential Load Forecasting Model Based on LSTM Recurrent Neural Network Considering Weather Features Open
With economic growth, the demand for power systems is increasingly large. Short-term load forecasting (STLF) becomes an indispensable factor to enhance the application of a smart grid (SG). Other than forecasting aggregated residential loa…
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Enhancing Electrical Load Prediction Using a Bidirectional LSTM Neural Network Open
Precise anticipation of electrical demand holds crucial importance for the optimal operation of power systems and the effective management of energy markets within the domain of energy planning. This study builds on previous research focus…
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Off-design analysis of a micro-CHP unit with solid oxide fuel cells fed by DME Open
This paper presents the results of stationary off-design modelling of a micro-combined heat and power unit with solid oxide fuel cells. Mathematical models of the main components of the system with nominal power output of 1.6 kW were devel…
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Monthly Electric Load Forecasting Using Transfer Learning for Smart Cities Open
Monthly electric load forecasting is essential to efficiently operate urban power grids. Although diverse forecasting models based on artificial intelligence techniques have been proposed with good performance, they require sufficient data…
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Short-term electricity load forecasting—A systematic approach from system level to secondary substations Open
Energy forecasting covers a wide range of prediction problems in the utility industry, such as forecasting demand, generation, price, and power load over time horizons and different power levels. Short-term load forecasting allows the syst…