Forecasting spot electricity prices: Deep learning approaches and empirical comparison of traditional algorithms

Random Forests

Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538

ImageNet classification with deep convolutional neural networks

Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155

Long Short-Term Memory

Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318

Adam: A Method for Stochastic Optimization

Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946

Scikit-learn: Machine Learning in Python

Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Müller, A., Nothman, J., Louppe, G., et al. (2012) · arXiv · doi:10.48550/arxiv.1201.0490 · Cited by 71,016

XGBoost: A Scalable Tree Boosting System

Chen, T., & Guestrin, C. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2939785 · Cited by 54,569

Regression shrinkage and selection via the lasso

Tibshirani, R. (1996) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1996.tb02080.x · Cited by 51,482

Untitled work

(2014) · Cited by 33,419

The Elements of Statistical Learning

Hastie, T., Friedman, J., & Tibshirani, R. (2001) · Springer Series in Statistics · doi:10.1007/978-0-387-21606-5 · Cited by 24,162

Multilayer feedforward networks are universal approximators

Hornik, K., Stinchcombe, M., & White, H. (1989) · Neural Networks · doi:10.1016/0893-6080(89)90020-8 · Cited by 21,728

Advanced price forecasting in agent-based electricity market simulation

Fraunholz, C., Kraft, E., Keles, D., & Fïchtner, W. (2021) · Applied Energy · doi:10.1016/j.apenergy.2021.116688 · Cited by 63

A robust auto encoder-gated recurrent unit (AE-GRU) based deep learning approach for short term solar power forecasting

Rai, A., Shrivastava, A., & Jana, K. C. (2021) · Optik · doi:10.1016/j.ijleo.2021.168515 · Cited by 63

Machine learning and internet of things applications in enterprise architectures: Solutions, challenges, and open issues

Rehman, Z., Tariq, N., Moqurrab, S. A., Yoo, J., & Srivastava, G. (2023) · Expert Systems · doi:10.1111/exsy.13467 · Cited by 63

DTTR: Encoding and decoding monthly runoff prediction model based on deep temporal attention convolution and multimodal fusion

Wang, W., Tian, W.-C., Hu, X.-X., Hong, Y.-H., Chai, F., & Xu, D. (2024) · Journal of Hydrology · doi:10.1016/j.jhydrol.2024.131996 · Cited by 62

Two-step deep learning framework with error compensation technique for short-term, half-hourly electricity price forecasting

Ghimire, S., Deo, R. C., Casillas-Pérez, D., & Salcedo‐Sanz, S. (2023) · Applied Energy · doi:10.1016/j.apenergy.2023.122059 · Cited by 61

Advanced forecasting and disturbance modelling for model predictive control of smart energy systems

Thilker, C. A., Madsen, H., & Jørgensen, J. B. (2021) · Applied Energy · doi:10.1016/j.apenergy.2021.116889 · Cited by 60

VMD-ATT-LSTM electricity price prediction based on grey wolf optimization algorithm in electricity markets considering renewable energy

Xu, Y., Huang, X., Zheng, X., Zeng, Z., & Jin, T. (2024) · Renewable Energy · doi:10.1016/j.renene.2024.121408 · Cited by 60

Prediction and explanation of the formation of the Spanish day-ahead electricity price through machine learning regression

Díaz, G., Coto, J., & Gómez‐Aleixandre, J. (2019) · Applied Energy · doi:10.1016/j.apenergy.2019.01.213 · Cited by 60

A hybrid deep learning-based online energy management scheme for industrial microgrid

Lu, R., Bai, R., Ding, Y., Wei, M., Jiang, J., Sun, M., Xiao, F., & Zhang, H. (2021) · Applied Energy · doi:10.1016/j.apenergy.2021.117857 · Cited by 61

A Survey on Deep Learning Role in Distribution Automation System: A New Collaborative Learning-to-Learning (L2L) Concept

Jafari, M., Kavousi‐Fard, A., Dabbaghjamanesh, M., & Karimi, M. (2022) · IEEE Access · doi:10.1109/access.2022.3195053 · Cited by 59

Heart Coronary Artery Segmentation and Disease Risk Warning Based on a Deep Learning Algorithm

Xiao, C., Li, Y., & Jiang, Y. (2020) · IEEE Access · doi:10.1109/access.2020.3010800 · Cited by 58

Day-Ahead Price Forecasting for Electricity Market using Long-Short Term Memory Recurrent Neural Network

Jiang, L., & Hu, G. (2018) · International Conference on Control, Automation, Robotics and Vision (ICARCV) · doi:10.1109/icarcv.2018.8581235 · Cited by 59

PLS-CNN-BiLSTM: An End-to-End Algorithm-Based Savitzky–Golay Smoothing and Evolution Strategy for Load Forecasting

Massaoudi, M., Refaat, S. S., Abu‐Rub, H., Chihi, I., & Oueslati, F. S. (2020) · Energies · doi:10.3390/en13205464 · Cited by 57

Next-Day Prediction of Hourly Solar Irradiance Using Local Weather Forecasts and LSTM Trained with Non-Local Data

Jeon, B.-K., & Kim, E.-J. (2020) · Energies · doi:10.3390/en13205258 · Cited by 56

Optimal Flexibility Dispatch Problem Using Second-Order Cone Relaxation of AC Power Flows

Torbaghan, S. S., Suryanarayana, G., Höschle, H., D’hulst, R., Geth, F., Caerts, C., & Hertem, D. V. (2019) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2019.2929845 · Cited by 55

Predicting coal price using time series methods and combination of radial basis function (RBF) neural network with time series

Sohrabi, P., Shokri, B. J., & Dehghani, H. (2021) · Mineral Economics · doi:10.1007/s13563-021-00286-z · Cited by 54

Optimization of microgrid operations using renewable energy sources

Agupugo, C. P., Kehinde, H. M., & Manuel, H. N. N. (2024) · Engineering Science & Technology Journal · doi:10.51594/estj.v5i7.1360 · Cited by 52

Profit-Sensitive Spatial Scheduling of Multi-Application Tasks in Distributed Green Clouds

Yuan, H., Bi, J., & Zhou, M. (2019) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2019.2909866 · Cited by 52

Forecasting day-ahead high-resolution natural-gas demand and supply in Germany

Chen, Y., Chua, W. S., & Koch, T. (2018) · Applied Energy · doi:10.1016/j.apenergy.2018.06.137 · Cited by 51

A deep spatial-temporal data-driven approach considering microclimates for power system security assessment

Huang, T., Guo, Q., Sun, H., Tan, C., & Hu, T. (2019) · Applied Energy · doi:10.1016/j.apenergy.2019.01.013 · Cited by 51

A Comparative Study Using Deep Learning and Support Vector Regression for Electricity Price Forecasting in Smart Grids

Atef, S., & Eltawil, A. (2019) · IEEE International Conference on Industrial Engineering and Applications (ICIEA) · doi:10.1109/iea.2019.8715213 · Cited by 54

A feature extraction- and ranking-based framework for electricity spot price forecasting using a hybrid deep neural network

Shao, Z., Zheng, Q., Liu, C., Gao, S., Wang, G., & Chu, Y. (2021) · Electric Power Systems Research · doi:10.1016/j.epsr.2021.107453 · Cited by 51

Scenarios modelling for forecasting day-ahead electricity prices: Case studies in Australia

Lu, X., Qiu, J., Lei, G., & Zhu, J. G. (2021) · Applied Energy · doi:10.1016/j.apenergy.2021.118296 · Cited by 55

Transfer learning for electricity price forecasting

Gündüz, S., Uğurlu, U., & Öksüz, İ. (2023) · Sustainable Energy Grids and Networks · doi:10.1016/j.segan.2023.100996 · Cited by 57

Using deep learning approaches with variable selection process to predict the energy performance of a heating and cooling system

Hwang, J. K., Yun, G. Y., Lee, S., Seo, H., & Santamouris, M. (2019) · Renewable Energy · doi:10.1016/j.renene.2019.10.113 · Cited by 52

Deep Learning for Variable Renewable Energy: A Systematic Review

Klaiber, J., & van Dinther, C. (2023) · ACM Computing Surveys · doi:10.1145/3586006 · Cited by 51

Electricity Price Forecasting for Cloud Computing Using an Enhanced Machine Learning Model

Albahli, S., Shiraz, M., & Ayub, N. (2020) · IEEE Access · doi:10.1109/access.2020.3035328 · Cited by 49

The role of data frequency and method selection in electricity price estimation: Comparative evidence from Turkey in pre-pandemic and pandemic periods

Depren, S. K., Kartal, M. T., Ertuğrul, H. M., & Depren, Ö. (2022) · Renewable Energy · doi:10.1016/j.renene.2021.12.136 · Cited by 48

A 1-dimensional continuous and smooth model for thermally stratified storage tanks including mixing and buoyancy

Lago, J., Ridder, F. D., Mazairac, W., & Schutter, B. D. (2019) · Applied Energy · doi:10.1016/j.apenergy.2019.04.139 · Cited by 48

Short-Term Forecasting for the Electricity Spot Prices With Extreme Values Treatment

Shah, I., Akbar, S., Saba, T., Ali, S., & Rehman, A. U. (2021) · IEEE Access · doi:10.1109/access.2021.3100076 · Cited by 47

On Stochastic Gradient Langevin Dynamics with Dependent Data Streams: The Fully Nonconvex Case

Chau, H. N., Moulines, É., Rásonyi, M., Sabanis, S., & Zhang, Y. (2021) · SIAM Journal on Mathematics of Data Science · doi:10.1137/20m1355392 · Cited by 47

TLIA: Time-series forecasting model using long short-term memory integrated with artificial neural networks for volatile energy markets

AL-Alimi, D., AlRassas, A. M., Al‐qaness, M. A. A., Cai, Z., Aseeri, A. O., Elaziz, M. A., & Ewees, A. A. (2023) · Applied Energy · doi:10.1016/j.apenergy.2023.121230 · Cited by 46

Artificial Intelligence and Mathematical Models of Power Grids Driven by Renewable Energy Sources: A Survey

Srinivasan, S., Kumarasamy, S., Andreadakis, Z., & Lind, P. G. (2023) · Energies · doi:10.3390/en16145383 · Cited by 46

Machine and deep learning approaches for forecasting electricity price and energy load assessment on real datasets

El-Azab, H.-A. I., Swief, R. A., El-Amary, N. H., & Temraz, H. K. (2024) · Ain Shams Engineering Journal · doi:10.1016/j.asej.2023.102613 · Cited by 46

Optimal dynamic pricing for an electricity retailer in the price-responsive environment of smart grid

Taherian, H., Aghaebrahimi, M. R., Baringo, L., & Goldani, S. R. (2021) · International Journal of Electrical Power & Energy Systems · doi:10.1016/j.ijepes.2021.107004 · Cited by 46

Locational Marginal Price Forecasting: A Componential and Ensemble Approach

Zheng, K., Wang, Y., Liu, K., & Chen, Q. (2020) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2020.2985070 · Cited by 45

Electricity Load Forecasting for Each Day of Week Using Deep CNN

Khan, S., Javaid, N., Chand, A., Khan, A. B. M., Rashid, F., & Afridi, I. U. (2019) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-15035-8_107 · Cited by 44

Day-ahead high-resolution forecasting of natural gas demand and supply in Germany with a hybrid model

Chen, Y., Xu, X., & Koch, T. (2020) · Applied Energy · doi:10.1016/j.apenergy.2019.114486 · Cited by 44

Recalibration of recurrent neural networks for short-term wind power forecasting

Toubeau, J., Dapoz, P.-D., Bottieau, J., Wautier, A., Grève, Z. D., & Vallée, F. (2020) · Electric Power Systems Research · doi:10.1016/j.epsr.2020.106639 · Cited by 44

Neural Network Based Model Comparison for Intraday Electricity Price Forecasting

Öksüz, İ., & Uğurlu, U. (2019) · Energies · doi:10.3390/en12234557 · Cited by 46

Use of new variables based on air temperature for forecasting day-ahead spot electricity prices using deep neural networks: A new approach

Jasiński, T. (2020) · Energy · doi:10.1016/j.energy.2020.118784 · Cited by 43

One month-ahead electricity price forecasting in the context of production planning

Windler, T., Busse, J., & Rieck, J. (2019) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2019.117910 · Cited by 42

Forecasting third-party mobile payments with implications for customer flow prediction

Ma, S., & Fildes, R. A. (2020) · International Journal of Forecasting · doi:10.1016/j.ijforecast.2019.08.012 · Cited by 42

Forecasting the occurrence of extreme electricity prices using a multivariate logistic regression model

Liu, L., Bai, F., Su, C., Ma, C., Yan, R., Li, H., Sun, Q., & Wennersten, R. (2022) · Energy · doi:10.1016/j.energy.2022.123417 · Cited by 42

Deep non-crossing probabilistic wind speed forecasting with multi-scale features

Zou, R., Song, M., Wang, Y., Wang, J., Yang, K., & Affenzeller, M. (2022) · Energy Conversion and Management · doi:10.1016/j.enconman.2022.115433 · Cited by 41

PSO Based Optimized Ensemble Learning and Feature Selection Approach for Efficient Energy Forecast

Shafqat, W., Malik, S., Lee, K., & Kim, D. (2021) · Electronics · doi:10.3390/electronics10182188 · Cited by 41

GHTnet: Tri-Branch deep learning network for real-time electricity price forecasting

Yang, H., & Schell, K. R. (2021) · Energy · doi:10.1016/j.energy.2021.122052 · Cited by 40

Deep Learning in Power Systems: A Bibliometric Analysis and Future Trends

Miraftabzadeh, S. M., Martino, A. D., Longo, M., & Zaninelli, D. (2024) · IEEE Access · doi:10.1109/access.2024.3491914 · Cited by 39

Short-Term Firm-Level Energy-Consumption Forecasting for Energy-Intensive Manufacturing: A Comparison of Machine Learning and Deep Learning Models

Ribeiro, A. M. N. C., do Carmo, P. R. X., Rodrigues, I. R., Sadok, D. F. H., Lynn, T., & Endo, P. T. (2020) · Algorithms · doi:10.3390/a13110274 · Cited by 41

Analyzing the influence of geopolitical risks on European power prices using a multiresolution causal neural network

Saâdaoui, F., & Jabeur, S. B. (2023) · Energy Economics · doi:10.1016/j.eneco.2023.106793 · Cited by 47

Price forecasting in the Ontario electricity market via TriConvGRU hybrid model: Univariate vs. multivariate frameworks

Ehsani, B., Pineau, P., & Charlin, L. (2024) · Applied Energy · doi:10.1016/j.apenergy.2024.122649 · Cited by 42

Flexible electricity price forecasting by switching mother wavelets based on wavelet transform and Long Short-Term Memory

Iwabuchi, K., Kato, K., Watari, D., Taniguchi, I., Catthoor, F., Shirazi, E., & Onoye, T. (2022) · Energy and AI · doi:10.1016/j.egyai.2022.100192 · Cited by 38

The Financial Effect of the Electricity Price Forecasts’ Inaccuracy on a Hydro-Based Generation Company

Uğurlu, U., Taş, O., Kaya, A., & Öksüz, İ. (2018) · Energies · doi:10.3390/en11082093 · Cited by 37

Locational Marginal Price Forecasting Using Deep Learning Network Optimized by Mapping-Based Genetic Algorithm

Hong, Y., Taylar, J. V., & Fajardo, A. C. (2020) · IEEE Access · doi:10.1109/access.2020.2994444 · Cited by 37

Short- and long-term forecasting of electricity prices using embedding of calendar information in neural networks

Wagner, A., Ramentol, E., Schirra, F., & Michaeli, H. (2022) · Journal of commodity markets · doi:10.1016/j.jcomm.2022.100246 · Cited by 45

Electricity Price Forecasting in European Day Ahead Markets: A Greedy Consideration of Market Integration

van der Heijden, T., Lago, J., Pálenský, P., & Abraham, E. (2021) · IEEE Access · doi:10.1109/access.2021.3108629 · Cited by 37

BRIM: An Accurate Electricity Spot Price Prediction Scheme-Based Bidirectional Recurrent Neural Network and Integrated Market

Chen, Y., Wang, Y., Ma, J., & Jin, Q. (2019) · Energies · doi:10.3390/en12122241 · Cited by 37

Day-Ahead Electricity Demand Forecasting Using a Novel Decomposition Combination Method

Iftikhar, H., Chaparro, J. E. T., Rodrigues, P. C., & López‐Gonzales, J. L. (2023) · Energies · doi:10.3390/en16186675 · Cited by 35

Forecasting electricity prices from the state-of-the-art modeling technology and the price determinant perspectives

Chai, S., Li, Q., Abedin, M. Z., & Lucey, B. M. (2023) · Research in International Business and Finance · doi:10.1016/j.ribaf.2023.102132 · Cited by 35

Averaging Predictive Distributions Across Calibration Windows for Day-Ahead Electricity Price Forecasting

Serafin, T., Uniejewski, B., & Weron, R. (2019) · Energies · doi:10.3390/en12132561 · Cited by 35

A Review on Machine Learning Models in Forecasting of Virtual Power Plant Uncertainties

Doğan, A., & Dogan, D. C. (2022) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-022-09860-2 · Cited by 35

Uncertainty reduction in power forecasting of virtual power plant: From day-ahead to balancing markets

Nadimi, R., & Goto, M. (2024) · Renewable Energy · doi:10.1016/j.renene.2024.121875 · Cited by 34

Modeling Intraday Markets under the New Advances of the Cross-Border Intraday Project (XBID): Evidence from the German Intraday Market

Kath, C. (2019) · Energies · doi:10.3390/en12224339 · Cited by 34

Electricity Load and Price Forecasting Using Machine Learning Algorithms in Smart Grid: A Survey

Arif, A., Javaid, N., Anwar, M., Naeem, A., Gul, H., & Fareed, S. (2020) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-44038-1_43 · Cited by 34

Deep distributional time series models and the probabilistic forecasting of intraday electricity prices

Klein, N., Smith, M. S., & Nott, D. J. (2023) · Journal of Applied Econometrics · doi:10.1002/jae.2959 · Cited by 34

Capturing Spatio-Temporal Dependencies in the Probabilistic Forecasting of Distribution Locational Marginal Prices

Toubeau, J., Morstyn, T., Bottieau, J., Zheng, K., Apostolopoulou, D., Grève, Z. D., Wang, Y., & Vallée, F. (2020) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2020.3047863 · Cited by 36

A Novel Hybrid Deep Learning Model for Sugar Price Forecasting Based on Time Series Decomposition

Zhang, J., Meng, Y., Wei, J., Chen, J., & Qin, J. (2021) · Mathematical Problems in Engineering · doi:10.1155/2021/6507688 · Cited by 35

Flexible process operation for electrified chemical plants

Cegla, M., Semrau, R., Tamagnini, F., & Engell, S. (2023) · Current Opinion in Chemical Engineering · doi:10.1016/j.coche.2023.100898 · Cited by 33

Introducing Technical Indicators to Electricity Price Forecasting: A Feature Engineering Study for Linear, Ensemble, and Deep Machine Learning Models

Demir, S., Mincev, K., Kok, K., & Paterakis, N. G. (2019) · Applied Sciences · doi:10.3390/app10010255 · Cited by 33

Analysis of the Fundamental Predictability of Prices in the British Balancing Market

Bunn, D. W., Inekwe, J. N., & MacGeehan, D. (2020) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2020.3015871 · Cited by 33

(Machine) Learning from the COVID-19 Lockdown about Electricity Market Performance with a Large Share of Renewables

Graf, C., Quaglia, F., & Wolak, F. A. (2020) · Journal of Environmental Economics and Management · doi:10.1016/j.jeem.2020.102398 · Cited by 33

A novel scheduling strategy for virtual power plant based on power market dynamic triggers

Wang, Y., Xu, C., Xie, D. G., Gu, C., Zhao, P., Gong, J., Pan, M., & Wang, X. (2023) · Applied Energy · doi:10.1016/j.apenergy.2023.121758 · Cited by 32

A novel probabilistic gradient boosting model with multi-approach feature selection and iterative seasonal trend decomposition for short-term load forecasting

Saini, P., & Parida, S. K. (2024) · Energy · doi:10.1016/j.energy.2024.130975 · Cited by 32

Comparison of the Deep Learning Performance for Short-Term Power Load Forecasting

Son, N. (2021) · Sustainability · doi:10.3390/su132212493 · Cited by 33

Deep learning-based channel quality indicators prediction for vehicular communication

Kim, J., & Han, D. S. (2022) · ICT Express · doi:10.1016/j.icte.2022.05.002 · Cited by 39

Systemic risk prediction using machine learning: Does network connectedness help prediction?

Wang, G., Chen, Y., Zhu, Y., & Xie, C. (2024) · International Review of Financial Analysis · doi:10.1016/j.irfa.2024.103147 · Cited by 32

A Trading-Based Evaluation of Density Forecasts in a Real-Time Electricity Market

Bunn, D. W., Gianfreda, A., & Kermer, S. (2018) · Energies · doi:10.3390/en11102658 · Cited by 31

Optimizing power-to-H2 participation in the Nord Pool electricity market: Effects of different bidding strategies on plant operation

Janke, L., McDonagh, S., Weinrich, S., Murphy, J. D., Nilsson, D., Hansson, P., & Nordberg, Å. (2020) · Renewable Energy · doi:10.1016/j.renene.2020.04.080 · Cited by 31

Application of deep learning and chaos theory for load forecasting in Greece

Stergiou, K., & Karakasidis, T. E. (2021) · Neural Computing and Applications · doi:10.1007/s00521-021-06266-2 · Cited by 31

Data-driven Two-step Day-ahead Electricity Price Forecasting Considering Price Spikes

Liu, S., Jiang, Y., Lin, Z., Wen, F., Ding, Y., & Li, Y. (2023) · Journal of Modern Power Systems and Clean Energy · doi:10.35833/mpce.2021.000196 · Cited by 31

Comprehensive commodity price forecasting framework using text mining methods

An, W., Wang, L., & Zhang, D. (2023) · Journal of Forecasting · doi:10.1002/for.2985 · Cited by 31

Seasonal forecasting of the hourly electricity demand applying machine and deep learning algorithms impact analysis of different factors

El-Azab, H.-A. I., Swief, R. A., El-Amary, N. H., & Temraz, H. K. (2025) · Scientific Reports · doi:10.1038/s41598-025-91878-0 · Cited by 30

Conformal Prediction for electricity price forecasting in the day-ahead and real-time balancing market

O’Connor, C., Bahloul, M., Rossi, R., Prestwich, S., & Visentin, A. (2025) · Energy and AI · doi:10.1016/j.egyai.2025.100571 · Cited by 30

A Review on Short-Term Electricity Price Forecasting Techniques for Energy Markets

Jiang, L., & Hu, G. (2018) · International Conference on Control, Automation, Robotics and Vision (ICARCV) · doi:10.1109/icarcv.2018.8581312 · Cited by 31

Optimal Control Strategies for Seasonal Thermal Energy Storage Systems With Market Interaction

Lago, J., Suryanarayana, G., Sogancioglu, E., & Schutter, B. D. (2020) · IEEE Transactions on Control Systems Technology · doi:10.1109/tcst.2020.3016077 · Cited by 30

A new fuzzy logic based approach for optimal household appliance scheduling based on electricity price and load consumption prediction

Atef, S., Ismail, N., & Eltawil, A. (2021) · Advances in Building Energy Research · doi:10.1080/17512549.2021.1873183 · Cited by 31

Methods, data sources and applications of the Artificial Intelligence in the Energy Poverty context: A review

López-Vargas, A., Ledezma, A., & Araceli, S. (2022) · Energy and Buildings · doi:10.1016/j.enbuild.2022.112233 · Cited by 30

A Hybrid Model for Multi-Day-Ahead Electricity Price Forecasting considering Price Spikes

Manfre, D., López, M. Z., Zareipour, H., & Quashie, M. (2023) · Forecasting · doi:10.3390/forecast5030028 · Cited by 33

Powering Electricity Forecasting with Transfer Learning

Kamalov, F., Sulieman, H., Moussa, S., Reyes, J. A., & Safaraliev, M. (2024) · Energies · doi:10.3390/en17030626 · Cited by 30

Performance of alternative electricity price forecasting methods: Findings from the Greek and Hungarian power exchanges

Halužan, M., Verbič, M., & Zorić, J. (2020) · Applied Energy · doi:10.1016/j.apenergy.2020.115599 · Cited by 35

Electricity Price Forecasting in the Irish Balancing Market

O’Connor, C., Collins, J., Prestwich, S., & Visentin, A. (2024) · Energy Strategy Reviews · doi:10.1016/j.esr.2024.101436 · Cited by 29

A hybrid framework for day-ahead electricity spot-price forecasting: A case study in China

Huang, S., Shi, J., Wang, B., An, N., Li, L., Hou, X., Wang, C., Zhang, X., Wang, K., Li, H., et al. (2024) · Applied Energy · doi:10.1016/j.apenergy.2024.123863 · Cited by 29

A review on deep anomaly detection in blockchain

Mounnan, O., Manad, O., Boubchir, L., Mouatasim, A. E., & Daâchi, B. (2024) · Blockchain Research and Applications · doi:10.1016/j.bcra.2024.100227 · Cited by 34

Research on data-driven identification and prediction of heat response time of urban centralized heating system

Zhong, W., Huang, W., Lin, X., Li, Z., & Zhou, Y. (2020) · Energy · doi:10.1016/j.energy.2020.118742 · Cited by 29

Benefits of short-term photovoltaic power production forecasting to the power system

Jakoplić, A., Franković, D., Kirinčić, V., & Plavšić, T. (2020) · Optimization and Engineering · doi:10.1007/s11081-020-09583-y · Cited by 29

Performances of Different Machine Learning Algorithms for Predicting Saltwater Intrusion in the Vietnamese Mekong Delta Using Limited Input Data: A Study from Ham Luong River

Tran, T. T., Pham, N. H., Pham, Q. B., Pham, L. T., Quảng, N. X., Nguyen, D.-T., Nguyen, P. N., & Veettil, B. K. (2022) · Water Resources · doi:10.1134/s0097807822030198 · Cited by 29

Electricity Price Forecasting with Neural Networks on EPEX Order Books

Schnürch, S., & Wagner, A. (2020) · Applied Mathematical Finance · doi:10.1080/1350486x.2020.1805337 · Cited by 32

Machine Learning for Energy Systems Optimization

Kim, I., Kim, B., & Sidorov, D. (2022) · Energies · doi:10.3390/en15114116 · Cited by 29

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