Comparison of ARIMA and ANN Models Used in Electricity Price Forecasting for Power Market

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Energy and Power Engineering

ISSN Print: 1949-243X
ISSN Online: 1947-3818
www.scirp.org/journal/epe
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"Comparison of ARIMA and ANN Models Used in Electricity Price Forecasting for Power Market"
written by Gao Gao, Kwoklun Lo, Fulin Fan,
published by Energy and Power Engineering, Vol.9 No.4B, 2017

has been cited by the following article(s):

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[12]	Algorithmic Approaches to Energy Market Price Prediction
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[14]	Electricity Price Instability over Time: Time Series Analysis and Forecasting
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[17]	Technical indicators for energy market trading
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[24]	Türkiye'de Gün Öncesi Piyasası İçin Elektrik Fiyatlarının Tahmini
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[28]	Forecasting Day-ahead Electricity Prices with A SARIMAX Model
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[1]	A Single-Buyer Model of Imbalance Cost Pass-Through Pricing Forecasting in the Malaysian Electricity Supply Industry Electricity, 2024 DOI:10.3390/electricity5020015

[2]	Balancing the grid: mitigating the effects of renewable energy in Italy via skew modeling and forecasting Annals of Operations Research, 2024 DOI:10.1007/s10479-024-06256-2

[3]	Combining varying training data-based artificial intelligence models for energy price forecasting Journal of the Chinese Institute of Engineers, 2023 DOI:10.1080/02533839.2023.2238761

[4]	Electricity price estimation using deep learning approaches: An empirical study on Turkish markets in normal and Covid-19 periods Expert Systems with Applications, 2023 DOI:10.1016/j.eswa.2023.120026

[5]	Time Series-Based Electric Load Forecasting with Mixture of Expert System 2023 4th International Conference on Intelligent Computing and Human-Computer Interaction (ICHCI), 2023 DOI:10.1109/ICHCI58871.2023.10277743

[6]	Predicting the Gap in the Day-Ahead and Real-Time Market Prices Leveraging Exogenous Weather Data Algorithms, 2023 DOI:10.3390/a16110508

[7]	A Hawkes Model Approach to Modeling Price Spikes in the Japanese Electricity Market Energies, 2023 DOI:10.3390/en16041570

[8]	Electricity price estimation using deep learning approaches: An empirical study on Turkish markets in normal and Covid-19 periods Expert Systems with Applications, 2023 DOI:10.1016/j.eswa.2023.120026

[9]	Electricity Price Instability over Time: Time Series Analysis and Forecasting Sustainability, 2022 DOI:10.3390/su14159081

[10]	The Economic Viability of PV Power Plant Based on a Neural Network Model of Electricity Prices Forecast: A Case of a Developing Market Energies, 2022 DOI:10.3390/en15176219

[11]	Hourly electricity price forecasting with NARMAX Machine Learning with Applications, 2022 DOI:10.1016/j.mlwa.2022.100383

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[13]	Applications of Artificial Intelligence in Planning and Operation of Smart Grids Power Systems, 2022 DOI:10.1007/978-3-030-94522-0_4

[14]	Short-term electricity consumption forecasting with NARX, LSTM, and SVR for a single building: small data set approach Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 2022 DOI:10.1080/15567036.2022.2104410

[15]	Hourly electricity price forecasting with NARMAX Machine Learning with Applications, 2022 DOI:10.1016/j.mlwa.2022.100383

[16]	Hourly electricity price forecasting with NARMAX Machine Learning with Applications, 2022 DOI:10.1016/j.mlwa.2022.100383

[17]	The Economic Viability of PV Power Plant Based on a Neural Network Model of Electricity Prices Forecast: A Case of a Developing Market Energies, 2022 DOI:10.3390/en15176219

[18]	Technical indicators for energy market trading Machine Learning with Applications, 2021 DOI:10.1016/j.mlwa.2021.100182

[19]	Artificial Intelligence in Intelligent Systems Lecture Notes in Networks and Systems, 2021 DOI:10.1007/978-3-030-77445-5_58

[20]	Time series modeling using Box-Jenkins model for shariah compliant healthcare sector in Malaysia Journal of Physics: Conference Series, 2021 DOI:10.1088/1742-6596/1988/1/012093

[21]	Technical indicators for energy market trading Machine Learning with Applications, 2021 DOI:10.1016/j.mlwa.2021.100182

[22]	Data-driven real-time price-based demand response for industrial facilities energy management Applied Energy, 2021 DOI:10.1016/j.apenergy.2020.116291

[23]	Yapay Sinir Ağı ve Box-Jenkins Modeli ile Yazıcı Sarf Malzemelerinin Analizi ve Modellenmesi: Irak Örneği Teknik Bilimler Dergisi, 2020 DOI:10.35354/tbed.664956

[24]	A hybrid optimized error correction system for time series forecasting Applied Soft Computing, 2020 DOI:10.1016/j.asoc.2019.105970

[25]	Yapay Sinir Ağı ve Box-Jenkins Modeli ile Yazıcı Sarf Malzemelerinin Analizi ve Modellenmesi: Irak Örneği Teknik Bilimler Dergisi, 2020 DOI:10.35354/tbed.664956

[26]	Development of Neurofuzzy Architectures for Electricity Price Forecasting Energies, 2020 DOI:10.3390/en13051209

[27]	Stock Market Price Forecasting using ARIMA vs ANN; A Case study from CSE 2020 2nd International Conference on Advancements in Computing (ICAC), 2020 DOI:10.1109/ICAC51239.2020.9357288

[28]	Development of Neurofuzzy Architectures for Electricity Price Forecasting Energies, 2020 DOI:10.3390/en13051209

[29]	Time Decomposition and Short-Term Forecasting of Hydrometeorological Conditions in the South Baltic Coastal Zone of Poland Geosciences, 2019 DOI:10.3390/geosciences9020068

[30]	Forecasting Day-ahead Electricity Prices with A SARIMAX Model 2019 IEEE Symposium Series on Computational Intelligence (SSCI), 2019 DOI:10.1109/SSCI44817.2019.9002930

[31]	Advances in Computational Intelligence Systems Advances in Intelligent Systems and Computing, 2019 DOI:10.1007/978-3-319-97982-3_6

[32]	Time Decomposition and Short-Term Forecasting of Hydrometeorological Conditions in the South Baltic Coastal Zone of Poland Geosciences, 2019 DOI:10.3390/geosciences9020068

[33]	Exploiting Artificial Neural Networks for the Prediction of Ancillary Energy Market Prices Energies, 2018 DOI:10.3390/en11071906

[34]	Exploiting Artificial Neural Networks for the Prediction of Ancillary Energy Market Prices Energies, 2018 DOI:10.3390/en11071906

[35]	A Novel Hybrid BND-FOA-LSSVM Model for Electricity Price Forecasting Information, 2017 DOI:10.3390/info8040120

[36]	A Novel Hybrid BND-FOA-LSSVM Model for Electricity Price Forecasting Information, 2017 DOI:10.3390/info8040120

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