Most cited papers in Energy Load and Power Forecasting
What are the most cited papers in Energy Load and Power Forecasting?
The most cited paper in Energy Load and Power Forecasting is "Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting" (Haoyi Zhou, Shanghang Zhang, Jieqi Peng et al., 2021), with 6,999 citations. Next come "Forecasting with artificial neural networks:" (4,238) and "Linear system theory and design" (3,686).
Showing 50 of 99 papers. Every paper with more than 759 citations is listed.
| # | Paper | ||
|---|---|---|---|
| 1 | Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting Haoyi Zhou, Shanghang Zhang, Jieqi Peng et al. · Proceedings of the AAAI Conference on Artificial Intelligence | 18 May 2021 | 6,999 |
| 2 | Forecasting with artificial neural networks: Guoqiang Zhang, B. Eddy Patuwo, Michael Y. Hu · International Journal of Forecasting | Mar 1998 | 4,238 |
| 3 | Linear system theory and design Stephen Mark Barnett · Automatica | May 1986 | 3,686 |
| 4 | Doubly fed induction generator usingback-to-back PWM convertersand its application to variable-speed wind-energy generation Ruben S. Pena, John Clare, Greg M Asher · IEE Proceedings - Electric Power Applications | 10 May 1996 | 2,766 |
| 5 | Short-Term Residential Load Forecasting Based on LSTM Recurrent Neural Network Weicong Kong, Zhao Yang Dong, Youwei Jia et al. · IEEE Transactions on Smart Grid | 18 Sep 2017 | 2,652 |
| 6 | Neural networks for the prediction and forecasting of water resources variables: a review of modelling issues and applications Holger Robert Maier, Graeme C. Dandy · Environmental Modelling & Software | 2000 | 2,404 |
| 7 | Neural networks for short-term load forecasting: a review and evaluation Henrique S. Hippert, Carlos Eduardo Pedreira, Reinaldo Castro Souza · IEEE Transactions on Power Systems | 2001 | 2,233 |
| 8 | A review on the prediction of building energy consumption Hai‐Xiang Zhao, Frédéric Magoulès · Renewable and Sustainable Energy Reviews | 28 Apr 2012 | 1,957 |
| 9 | A review of data-driven building energy consumption prediction studies Kadir Amasyali, Nora El-Gohary · Renewable and Sustainable Energy Reviews | 12 Sep 2017 | 1,870 |
| 10 | Doubly Fed Induction Generator Systems for Wind Turbines: A Viable Alternative to Adjust Speed over a Wide Range at Minimal Cost Sascha Müller, M. Deicke, Rik W. De Doncker · IEEE Industry Applications Magazine | May 2002 | 1,870 |
| 11 | Machine learning methods for solar radiation forecasting: A review Cyril Voyant, Gilles Notton, Soteris A. Kalogirou et al. · Renewable Energy | 4 Jan 2017 | 1,810 |
| 12 | Financial time series forecasting using support vector machines Kyoung-jae Kim · Neurocomputing | 19 May 2003 | 1,741 |
| 13 | Particle swarm optimization to solving the economic dispatch considering the generator constraints Z.-L. Gaing · IEEE Transactions on Power Systems | 29 Jul 2003 | 1,723 |
| 14 | A study of the characteristics of white noise using the empirical mode decomposition method Zhaohua Wu, Norden Eh Huang · Proceedings of the Royal Society A Mathematical Physical and Engineering Sciences | 5 Apr 2004 | 1,722 |
| 15 | Long-term patterns of European PV output using 30 years of validated hourly reanalysis and satellite data Stefan Pfenninger, Iain Staffell · Energy | 8 Sep 2016 | 1,720 |
| 16 | Adaptive Robust Optimization for the Security Constrained Unit Commitment Problem Dimitris Bertsimas, Eugene Litvinov, Xu Andy Sun et al. · IEEE Transactions on Power Systems | 24 Jul 2012 | 1,676 |
| 17 | The Performance of LSTM and BiLSTM in Forecasting Time Series Sima Siami‐Namini, Neda Beitollahi Tavakoli, Akbar Siami Namin | Dec 2019 | 1,583 |
| 18 | ARIMA models to predict next-day electricity prices Javier Contreras, Rosa Espínola, Francisco J. Nogales et al. · IEEE Transactions on Power Systems | 29 Jul 2003 | 1,556 |
| 19 | Using bias-corrected reanalysis to simulate current and future wind power output Iain Staffell, Stefan Pfenninger · Energy | 4 Sep 2016 | 1,518 |
| 20 | Predicting residential energy consumption using CNN-LSTM neural networks Tae Young Kim, Sung-Bae Cho · Energy | 4 Jun 2019 | 1,483 |
| 21 | Electric load forecasting using an artificial neural network Dong-Chul Park, M.A. El-Sharkawi, Robert J. Marks et al. · IEEE Transactions on Power Systems | May 1991 | 1,427 |
| 22 | Probabilistic electric load forecasting: A tutorial review Tao Hong, Fan Shu · International Journal of Forecasting | 19 Feb 2016 | 1,387 |
| 23 | Why You Should Never Use the Hodrick-Prescott Filter James Douglas Hamilton · The Review of Economics and Statistics | 11 Aug 2017 | 1,374 |
| 24 | Recurrent Neural Networks for Time Series Forecasting: Current status and future directions Hansika Hewamalage, Christoph Bergmeir, Kasun Bandara · International Journal of Forecasting | 10 Aug 2020 | 1,357 |
| 25 | Review of Smart Meter Data Analytics: Applications, Methodologies, and Challenges Yi Wang, Qixin Chen, Tao Hong et al. · IEEE Transactions on Smart Grid | 22 Mar 2018 | 1,355 |
| 26 | Review of photovoltaic power forecasting J. Antonanzas, Natalia Osorio, Rodrigo A. Escobar et al. · Solar Energy | 8 Jul 2016 | 1,334 |
| 27 | Current methods and advances in forecasting of wind power generation Aoife M Foley, Paul Leahy, Antonino Marvuglia et al. · Renewable Energy | 5 Jul 2011 | 1,313 |
| 28 | A Comparison of ARIMA and LSTM in Forecasting Time Series Sima Siami‐Namini, Neda Beitollahi Tavakoli, Akbar Siami Namin | Dec 2018 | 1,276 |
| 29 | Wind Turbines Emulating Inertia and Supporting Primary Frequency Control Johan Morren, Sjoerd W. H. de Haan, W.L. Kling et al. · IEEE Transactions on Power Systems | 31 Jan 2006 | 1,269 |
| 30 | Artificial intelligence in sustainable energy industry: Status Quo, challenges and opportunities Tanveer Ahmad, Dongdong Zhang, Chao Huang et al. · Journal of Cleaner Production | 9 Jan 2021 | 1,242 |
| 31 | A review and evaluation of the state-of-the-art in PV solar power forecasting: Techniques and optimization Razin Ahmed, Victor Sreeram, Yateendra Mishra et al. · Renewable and Sustainable Energy Reviews | 2 Mar 2020 | 1,240 |
| 32 | Forecasting of photovoltaic power generation and model optimization: A review Utpal Kumar Das, Kok Soon Tey, Mehdi Seyedmahmoudian et al. · Renewable and Sustainable Energy Reviews | Sep 2017 | 1,212 |
| 33 | A review of deep learning for renewable energy forecasting Huaizhi Wang, Zhenxing Lei, Xian Zhang et al. · Energy Conversion and Management | 17 Jul 2019 | 1,193 |
| 34 | Energy models for demand forecasting—A review L. Suganthi, Anand A. Samuel · Renewable and Sustainable Energy Reviews | 7 Oct 2011 | 1,190 |
| 35 | A Particle Swarm Optimization for Economic Dispatch With Nonsmooth Cost Functions J.-B. Park, Kwang‐Sik Lee, Joong-Rin Shin et al. · IEEE Transactions on Power Systems | 31 Jan 2005 | 1,170 |
| 36 | A review on the forecasting of wind speed and generated power Lei Ma, Luan Shiyan, Jiang Chuanwen et al. · Renewable and Sustainable Energy Reviews | 7 Mar 2008 | 1,165 |
| 37 | A new metric of absolute percentage error for intermittent demand forecasts Sung‐Il Kim, Heeyoung Kim · International Journal of Forecasting | 16 Mar 2016 | 1,150 |
| 38 | Time Series Prediction Using Support Vector Machines: A Survey Nicholas I. Sapankevych, Ravi T. Sankar · IEEE Computational Intelligence Magazine | 24 Apr 2009 | 1,144 |
| 39 | Predicting stock and stock price index movement using Trend Deterministic Data Preparation and machine learning techniques Jigar Patel, Sahil S. Shah, Priyank Thakkar et al. · Expert Systems with Applications | 5 Aug 2014 | 1,138 |
| 40 | A deep learning framework for financial time series using stacked autoencoders and long-short term memory Wei Bao, Jun Yue, Yulei Rao · PLoS ONE | 14 Jul 2017 | 1,135 |
| 41 | Deep Learning for Household Load Forecasting—A Novel Pooling Deep RNN Heng Shi, Minghao Xu, Ran Li · IEEE Transactions on Smart Grid | 23 Mar 2017 | 1,117 |
| 42 | Designing a neural network for forecasting financial and economic time series Iebeling Kaastra, Milton S. Boyd · Neurocomputing | Apr 1996 | 1,099 |
| 43 | An Economic Dispatch Model Incorporating Wind Power John Hetzer, David C. Yu, Kalu Bhattarai · IEEE Transactions on Energy Conversion | 30 Apr 2008 | 1,060 |
| 44 | Probabilistic energy forecasting: Global Energy Forecasting Competition 2014 and beyond Tao Hong, Pierre Pinson, Fan Shu et al. · International Journal of Forecasting | 12 Mar 2016 | 1,054 |
| 45 | Stock Price Prediction Using the ARIMA Model Adebiyi A. Ariyo, Aderemi O. Adewumi, Charles Korede Ayo | Mar 2014 | 1,051 |
| 46 | A review on artificial intelligence based load demand forecasting techniques for smart grid and buildings Muhammad Qamar Raza, Abbas Khosravi · Renewable and Sustainable Energy Reviews | 15 Jun 2015 | 1,023 |
| 47 | Trees vs Neurons: Comparison between random forest and ANN for high-resolution prediction of building energy consumption Muhammad Waseem Ahmad, Monjur M. Mourshed, Yacine Rezgui · Energy and Buildings | 22 Apr 2017 | 1,021 |
| 48 | A review on time series forecasting techniques for building energy consumption Chirag Deb, Fan Zhang, Junjing Yang et al. · Renewable and Sustainable Energy Reviews | 10 Mar 2017 | 1,014 |
| 49 | A hybrid ARIMA and support vector machines model in stock price forecasting Ping‐Feng Pai, Chih-Sheng Lin · Omega | 18 Sep 2004 | 1,010 |
| 50 | Artificial intelligence techniques for photovoltaic applications: A review A. Mellit, Soteris A. Kalogirou · Progress in Energy and Combustion Science | 6 Mar 2008 | 1,000 |
Citations are OpenAlex's own counts, from its 2026-09-23 snapshot, so the ranking can differ from Google Scholar or Web of Science. A paper is listed for a field when OpenAlex files it under one of the field's topics; retracted papers and papers whose citation count cannot be traced to citing works are left out, and a preprint and the article it became are shown once. Release dates are OpenAlex's: where a source gave only a month or a year, that is what is shown. How the lists are chosen.