4.8 Article

Short-term electricity load forecasting-A systematic approach from system level to secondary substations

Related references

Note: Only part of the references are listed.
Article Thermodynamics

Meta-ANN e A dynamic artificial neural network refined by meta- learning for Short-Term Load Forecasting

Xun Xiao et al.

Summary: In this paper, a dynamic Artificial Neural Network (ANN) model called Meta-ANN is developed to forecast the short-term grid load. The model uses a base module trained on historical data to learn the long-term trend and seasonality of the grid load, and integrates an error-correction module to capture the nonstationary pattern of the grid load. The experimental results show that Meta-ANN can make more accurate predictions by effectively capturing the nonstationary pattern in grid loads.

ENERGY (2022)

Review Energy & Fuels

Review of low voltage load forecasting: Methods, applications, and recommendations

Stephen Haben et al.

Summary: The increased digitalisation and monitoring of the energy system offer numerous opportunities for decarbonisation, especially through applications on low voltage, local networks. Reliable forecasting is crucial for these systems to anticipate key features and uncertainties. This paper aims to provide an overview of the current landscape, challenges, and recommendations for low voltage level forecasts to facilitate further research and development.

APPLIED ENERGY (2021)

Article Economics

Temporal Fusion Transformers for interpretable multi-horizon time series forecasting

Bryan Lim et al.

Summary: This paper introduces the Temporal Fusion Transformer (TFT), a novel attention-based architecture that combines high-performance multi-horizon forecasting with interpretable insights into temporal dynamics. TFT utilizes recurrent layers for local processing and interpretable self-attention layers for long-term dependencies, achieving high performance in a wide range of scenarios. By selecting relevant features and suppressing unnecessary components, TFT demonstrates significant performance improvements over existing benchmarks on various real-world datasets.

INTERNATIONAL JOURNAL OF FORECASTING (2021)

Article Energy & Fuels

A meta-learning based distribution system load forecasting model selection framework

Yiyan Li et al.

Summary: This study introduces a meta-learning based automatic distribution system load forecasting model selection framework, which optimizes model selection by considering load forecasting needs and data characteristics. Experimental results show that this method performs well in different load forecasting tasks.

APPLIED ENERGY (2021)

Article Engineering, Electrical & Electronic

Short-term load forecasting of industrial customers based on SVMD and XGBoost

Yuanyuan Wang et al.

Summary: An adaptive decomposition method based on VMD and SampEn (SVMD) is proposed for short-term load forecasting for industrial customers, combining XGBoost and linear regression models to establish prediction models. Relevant factors influencing industrial customers' electricity consumption behavior are considered to enhance accuracy, and test results show that the method significantly outperforms existing load forecasting models.

INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS (2021)

Article Biotechnology & Applied Microbiology

A green hydrogen economy for a renewable energy society

Alexandra M. Oliveira et al.

Summary: The concept of a hydrogen economy as part of a low-carbon future has been long promoted, but there is little agreement on its exact implications. Research suggests that hydrogen can play a significant role in decarbonization across various sectors such as industry, transportation, buildings, and power.

CURRENT OPINION IN CHEMICAL ENGINEERING (2021)

Article Computer Science, Information Systems

Individualized Short-Term Electric Load Forecasting With Deep Neural Network Based Transfer Learning and Meta Learning

Eunjung Lee et al.

Summary: It is generally believed that individualized models are the best way to predict electric load, but traditional methods tend to favor one-for-all models. This study utilizes transfer learning and meta learning, successfully integrated into deep neural networks, to form high-performance individualized models using individual data in just a few days.

IEEE ACCESS (2021)

Article Economics

Forecasting high resolution electricity demand data with additive models including smooth and jagged components

Umberto Amato et al.

Summary: Short-term load forecasting is essential for efficient operational management and planning of electric utilities. Emerging smart grid technologies bring new challenges and opportunities in load forecasting. Semi-parametric generalized additive models are increasingly popular due to their accuracy, flexibility, and interpretability, especially for forecasting at higher levels of aggregation.

INTERNATIONAL JOURNAL OF FORECASTING (2021)

Article Engineering, Electrical & Electronic

Joint bagged-boosted artificial neural networks: Using ensemble machine learning to improve short-term electricity load forecasting

A. S. Khwaja et al.

ELECTRIC POWER SYSTEMS RESEARCH (2020)

Review Energy & Fuels

Energy Forecasting: A Review and Outlook

Tao Hong et al.

IEEE OPEN ACCESS JOURNAL OF POWER AND ENERGY (2020)

Article Engineering, Electrical & Electronic

Short-Term Residential Load Forecasting Based on LSTM Recurrent Neural Network

Weicong Kong et al.

IEEE TRANSACTIONS ON SMART GRID (2019)

Article Green & Sustainable Science & Technology

Assessing Increased Flexibility of Energy Storage and Demand Response to Accommodate a High Penetration of Renewable Energy Sources

Ahmad Nikoobakht et al.

IEEE TRANSACTIONS ON SUSTAINABLE ENERGY (2019)

Review Green & Sustainable Science & Technology

A review on the selected applications of forecasting models in renewable power systems

Adil Ahmed et al.

RENEWABLE & SUSTAINABLE ENERGY REVIEWS (2019)

Editorial Material Robotics

XAI-Explainable artificial intelligence

David Gunning et al.

SCIENCE ROBOTICS (2019)

Review Energy & Fuels

Impacts of Demand-Side Management on Electrical Power Systems: A Review

Hussein Jumma Jabir et al.

ENERGIES (2018)

Review Construction & Building Technology

Electrical load forecasting models: A critical systematic review

Corentin Kuster et al.

SUSTAINABLE CITIES AND SOCIETY (2017)

Article Mathematics, Interdisciplinary Applications

Energy consumption prediction using people dynamics derived from cellular network data

Andrey Bogomolov et al.

EPJ DATA SCIENCE (2016)

Article Economics

Probabilistic electric load forecasting: A tutorial review

Tao Hong et al.

INTERNATIONAL JOURNAL OF FORECASTING (2016)

Article Computer Science, Artificial Intelligence

Pattern similarity-based methods for short-term load forecasting Part 1: Principles

Grzegorz Dudek

APPLIED SOFT COMPUTING (2015)

Article Engineering, Electrical & Electronic

Development of Low Voltage Network Templates-Part I: Substation Clustering and Classification

Ran Li et al.

IEEE TRANSACTIONS ON POWER SYSTEMS (2015)

Article Engineering, Electrical & Electronic

Development of Low Voltage Network Templates-Part II: Peak Load Estimation by Clusterwise Regression

Ran Li et al.

IEEE TRANSACTIONS ON POWER SYSTEMS (2015)

Review Green & Sustainable Science & Technology

A review on artificial intelligence based load demand forecasting techniques for smart grid and buildings

Muhammad Qamar Raza et al.

RENEWABLE & SUSTAINABLE ENERGY REVIEWS (2015)

Article Computer Science, Artificial Intelligence

Fuzzy interaction regression for short term load forecasting

Tao Hong et al.

FUZZY OPTIMIZATION AND DECISION MAKING (2014)

Article Computer Science, Information Systems

A Survey on Electric Power Demand Forecasting: Future Trends in Smart Grids, Microgrids and Smart Buildings

Luis Hernandez et al.

IEEE COMMUNICATIONS SURVEYS AND TUTORIALS (2014)

Article Economics

A new error measure for forecasts of household-level, high resolution electrical energy consumption

Stephen Haben et al.

INTERNATIONAL JOURNAL OF FORECASTING (2014)

Article Computer Science, Artificial Intelligence

Gradient boosting machines, a tutorial

Alexey Natekin et al.

FRONTIERS IN NEUROROBOTICS (2013)

Article Engineering, Electrical & Electronic

Interval Type-2 Fuzzy Logic Systems for Load Forecasting: A Comparative Study

Abbas Khosravi et al.

IEEE TRANSACTIONS ON POWER SYSTEMS (2012)

Review Green & Sustainable Science & Technology

Energy models for demand forecasting-A review

L. Suganthi et al.

RENEWABLE & SUSTAINABLE ENERGY REVIEWS (2012)

Article Engineering, Electrical & Electronic

Electric Load Forecasting Based on Statistical Robust Methods

Yacine Chakhchoukh et al.

IEEE TRANSACTIONS ON POWER SYSTEMS (2011)

Article Computer Science, Artificial Intelligence

Forecasting time series using a methodology based on autoregressive integrated moving average and genetic programming

Yi-Shian Lee et al.

KNOWLEDGE-BASED SYSTEMS (2011)

Article Computer Science, Artificial Intelligence

Power load forecasting using support vector machine and ant colony optimization

Dongxiao Niu et al.

EXPERT SYSTEMS WITH APPLICATIONS (2010)

Article Management

Electric load forecasting methods: Tools for decision making

Heiko Hahn et al.

EUROPEAN JOURNAL OF OPERATIONAL RESEARCH (2009)

Article Economics

Another look at measures of forecast accuracy

Rob J. Hyndman et al.

INTERNATIONAL JOURNAL OF FORECASTING (2006)

Article Engineering, Electrical & Electronic

Load forecasting using support vector machines: A study on EUNITE competition 2001

BJ Chen et al.

IEEE TRANSACTIONS ON POWER SYSTEMS (2004)

Review Automation & Control Systems

Electric load forecasting: literature survey and classification of methods

HK Alfares et al.

INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE (2002)

Article Statistics & Probability

Additive logistic regression: A statistical view of boosting

J Friedman et al.

ANNALS OF STATISTICS (2000)