/awesome-energy-forecasting

list of papers, code, and other resources

MIT LicenseMIT

awesome-energy-forecasting

list of papers, code, and other resources focus on energy forecasting.

Table of Contents

Competition

Papers

2021

  • Guermoui, M., Melgani, F., Gairaa, K., & Mekhalfi, M. L. (2020). A comprehensive review of hybrid models for solar radiation forecasting. Journal of Cleaner Production, 258, 120357. review
  • Mashlakov, A., Kuronen, T., Lensu, L., Kaarna, A., & Honkapuro, S. (2021). Assessing the performance of deep learning models for multivariate probabilistic energy forecasting. Applied Energy, 285, 116405.
  • Kazmi, H., Munné-Collado, Í., Mehmood, F., Syed, T. A., & Driesen, J. (2021). Towards data-driven energy communities: A review of open-source datasets, models and tools. Renewable and Sustainable Energy Reviews, 148, 111290. nice review
  • Munkhammar, J., van der Meer, D., & Widén, J. (2021). Very short term load forecasting of residential electricity consumption using the Markov-chain mixture distribution (MCM) model. Applied Energy, 282, 116180.
  • Li, L., Meinrenken, C. J., Modi, V., & Culligan, P. J. (2021). Short-term apartment-level load forecasting using a modified neural network with selected auto-regressive features. Applied Energy, 287, 116509.
  • Zhang, W., Chen, Q., Yan, J., Zhang, S., & Xu, J. (2021). A novel asynchronous deep reinforcement learning model with adaptive early forecasting method and reward incentive mechanism for short-term load forecasting. Energy, 121492. reinforcement learning
  • Du Plessis, A. A., Strauss, J. M., & Rix, A. J. (2021). Short-term solar power forecasting: Investigating the ability of deep learning models to capture low-level utility-scale Photovoltaic system behaviour. Applied Energy, 285, 116395.
  • Ding, P., Liu, X., Li, H., Huang, Z., Zhang, K., Shao, L., & Abedinia, O. (2021). Useful life prediction based on wavelet packet decomposition and two-dimensional convolutional neural network for lithium-ion batteries. Renewable and Sustainable Energy Reviews, 148, 111287.

2020

  • Dai, Y., & Zhao, P. (2020). A hybrid load forecasting model based on support vector machine with intelligent methods for feature selection and parameter optimization. Applied Energy, 279, 115332.

  • Ahmad, W., Ayub, N., Ali, T., Irfan, M., Awais, M., Shiraz, M., & Glowacz, A. (2020). Towards Short Term Electricity Load Forecasting Using Improved Support Vector Machine and Extreme Learning Machine. Energies, 13(11), 2907.

  • Yagli, G. M., Yang, D., Gandhi, O., & Srinivasan, D. (2020). Can we justify producing univariate machine-learning forecasts with satellite-derived solar irradiance?. Applied Energy, 259, 114122.

  • Yagli, G. M., Yang, D., & Srinivasan, D. (2019). Automatic hourly solar forecasting using machine learning models. Renewable and Sustainable Energy Reviews, 105, 487-498.

  • Li, P., Zhou, K., Lu, X., & Yang, S. (2020). A hybrid deep learning model for short-term PV power forecasting. Applied Energy, 259, 114216. clear

  • Ahmed, R., Sreeram, V., Mishra, Y., & Arif, M. D. (2020). A review and evaluation of the state-of-the-art in PV solar power forecasting: Techniques and optimization. Renewable and Sustainable Energy Reviews, 124, 109792. review

  • Wu, C., Wang, J., Chen, X., Du, P., & Yang, W. (2020). A novel hybrid system based on multi-objective optimization for wind speed forecasting. Renewable Energy, 146, 149-165.

  • Sun, W., & Huang, C. (2020). A carbon price prediction model based on secondary decomposition algorithm and optimized back propagation neural network. Journal of Cleaner Production, 243, 118671.

  • Liu, Z., Jiang, P., Zhang, L., & Niu, X. (2020). A combined forecasting model for time series: Application to short-term wind speed forecasting. Applied Energy, 259, 114137.

  • Nam, K., Hwangbo, S., & Yoo, C. (2020). A deep learning-based forecasting model for renewable energy scenarios to guide sustainable energy policy: A case study of Korea. Renewable and Sustainable Energy Reviews, 122, 109725.

  • Somu, N., MR, G. R., & Ramamritham, K. (2020). A hybrid model for building energy consumption forecasting using long short term memory networks. Applied Energy, 261, 114131.

  • Aly, H. H. (2020). A novel approach for harmonic tidal currents constitutions forecasting using hybrid intelligent models based on clustering methodologies. Renewable Energy, 147, 1554-1564.

  • Kong, W., Jia, Y., Dong, Z. Y., Meng, K., & Chai, S. (2020). Hybrid approaches based on deep whole-sky-image learning to photovoltaic generation forecasting. Applied Energy, 280, 115875. good idea

high citation paper (>100)

  • Kong, W., Dong, Z. Y., Jia, Y., Hill, D. J., Xu, Y., & Zhang, Y. (2017). Short-term residential load forecasting based on LSTM recurrent neural network. IEEE Transactions on Smart Grid, 10(1), 841-851.
  • Kong, W., Dong, Z. Y., Hill, D. J., Luo, F., & Xu, Y. (2017). Short-term residential load forecasting based on resident behaviour learning. IEEE Transactions on Power Systems, 33(1), 1087-1088.
  • Kim, T. Y., & Cho, S. B. (2019). Predicting residential energy consumption using CNN-LSTM neural networks. Energy, 182, 72-81.
  • Wang, Y., Gan, D., Sun, M., Zhang, N., Lu, Z., & Kang, C. (2019). Probabilistic individual load forecasting using pinball loss guided LSTM. Applied Energy, 235, 10-20.
  • Bedi, J., & Toshniwal, D. (2019). Deep learning framework to forecast electricity demand. Applied energy, 238, 1312-1326.
  • Qing, X., & Niu, Y. (2018). Hourly day-ahead solar irradiance prediction using weather forecasts by LSTM. Energy, 148, 461-468.
  • Wang, K., Qi, X., & Liu, H. (2019). A comparison of day-ahead photovoltaic power forecasting models based on deep learning neural network. Applied Energy, 251, 113315.
  • Lahouar, A., & Slama, J. B. H. (2017). Hour-ahead wind power forecast based on random forests. Renewable energy, 109, 529-541.
  • Du, P., Wang, J., Yang, W., & Niu, T. (2019). A novel hybrid model for short-term wind power forecasting. Applied Soft Computing, 80, 93-106.
  • Rolnick, D., Donti, P. L., Kaack, L. H., Kochanski, K., Lacoste, A., Sankaran, K., ... & Bengio, Y. (2019). Tackling climate change with machine learning. arXiv preprint arXiv:1906.05433. good review
  • Voyant, C., Notton, G., Kalogirou, S., Nivet, M. L., Paoli, C., Motte, F., & Fouilloy, A. (2017). Machine learning methods for solar radiation forecasting: A review. Renewable Energy, 105, 569-582. 700+
  • Pedro, H. T., & Coimbra, C. F. (2012). Assessment of forecasting techniques for solar power production with no exogenous inputs. Solar Energy, 86(7), 2017-2028.
  • Sobri, S., Koohi-Kamali, S., & Rahim, N. A. (2018). Solar photovoltaic generation forecasting methods: A review. Energy Conversion and Management, 156, 459-497. review
  • Makridakis, S., Spiliotis, E., & Assimakopoulos, V. (2018). Statistical and Machine Learning forecasting methods: Concerns and ways forward. PloS one, 13(3), e0194889.
  • Rustam, F., Reshi, A. A., Mehmood, A., Ullah, S., On, B. W., Aslam, W., & Choi, G. S. (2020). COVID-19 future forecasting using supervised machine learning models. IEEE access, 8, 101489-101499.
  • Hewamalage, H., Bergmeir, C., & Bandara, K. (2021). Recurrent neural networks for time series forecasting: Current status and future directions. International Journal of Forecasting, 37(1), 388-427.

Journals

  • Applied Energy
  • Fuel
  • Energy Convertion and Management
  • Journal of Cleaner Production
  • Renewable Energy
  • Solor Energy
  • Energy
  • Renewable and Sustainable Energy Reviews

Code

Datasets