Expect: EXplainable Prediction Model for Energy ConsumpTion

Long Short-Term Memory

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

Greedy function approximation: A gradient boosting machine

Friedman, J. H. (2001) · The Annals of Statistics · doi:10.1214/aos/1013203451 · Cited by 30,731 · see citing works

"Why Should I Trust You?": Explaining the Predictions of Any Classifier

Ribeiro, M. T., Singh, S., & Guestrin, C. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2939778 · Cited by 17,555 · see citing works

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

Kong, W., Dong, Z. Y., Jia, Y., Hill, D. J., Xu, Y. H., & Zhang, Y. (2017) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2017.2753802 · Cited by 2,678 · see citing works

Predicting residential energy consumption using CNN-LSTM neural networks

Kim, T. Y., & Cho, S.-B. (2019) · Energy · doi:10.1016/j.energy.2019.05.230 · Cited by 1,492 · see citing works

Generación de series de tiempo financieras sintéticas para "data augmentation" usando redes neuronales generativas adversarias (GAN)

Villarraga Ossa, E. F. (2021) · LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) · Cited by 1,472 · see citing works

A review on time series forecasting techniques for building energy consumption

Deb, C., Zhang, F., Yang, J., Lee, S. E., & Shah, K. W. (2017) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2017.02.085 · Cited by 1,032 · see citing works

A survey on deep learning methods for power load and renewable energy forecasting in smart microgrids

Aslam, S., Herodotou, H., Mohsin, S. M., Javaid, N., Ashraf, N., & Aslam, S. (2021) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2021.110992 · Cited by 677 · see citing works

Regression analysis for prediction of residential energy consumption

Fumo, N., & Biswas, M. A. R. (2015) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2015.03.035 · Cited by 632 · see citing works

Vector field-based support vector regression for building energy consumption prediction

Zhong, H., Wang, J., Jia, H., Mu, Y., & Lv, S. (2019) · Applied Energy · doi:10.1016/j.apenergy.2019.03.078 · Cited by 386 · see citing works

Forecasting methods in energy planning models

Debnath, K. B., & Mourshed, M. M. (2018) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2018.02.002 · Cited by 348 · see citing works

Time Series Forecasting Using Hybrid ARIMA and ANN Models Based on DWT Decomposition

Khandelwal, I., Adhikari, R., & Verma, G. (2015) · Procedia Computer Science · doi:10.1016/j.procs.2015.04.167 · Cited by 287 · see citing works

A big data driven framework for demand-driven forecasting with effects of marketing-mix variables

Kumar, A., Shankar, R., & Aljohani, N. R. (2019) · Industrial Marketing Management · doi:10.1016/j.indmarman.2019.05.003 · Cited by 152 · see citing works

Electric Energy Consumption Prediction by Deep Learning with State Explainable Autoencoder

Kim, J. Y., & Cho, S.-B. (2019) · Energies · doi:10.3390/en12040739 · Cited by 149 · see citing works

Artificial neural network decision support tool for assessment of the energy performance and the refurbishment actions for the non-residential building stock in Southern Italy

Beccali, M., Ciulla, G., Brano, V. L., Galatioto, A., & Bonomolo, M. (2017) · Energy · doi:10.1016/j.energy.2017.05.200 · Cited by 100 · see citing works

A hybrid neuro-fuzzy inference system-based algorithm for time series forecasting applied to energy consumption prediction

Jallal, M. A., González-Vidal, A., Skármeta, A., Chabaa, S., & Zeroual, A. (2020) · Applied Energy · doi:10.1016/j.apenergy.2020.114977 · Cited by 99 · see citing works

Explainable boosted linear regression for time series forecasting

Ilic, I., Görgülü, B., Çevik, M., & Baydoğan, M. G. (2021) · Pattern Recognition · doi:10.1016/j.patcog.2021.108144 · Cited by 84 · see citing works

Estimation and Forecasts for the Share of Renewable Energy Consumption in Final Energy Consumption by 2020 in the European Union

Mehedinţu, A., Sterpu, M., & Șoavă, G. (2018) · Sustainability · doi:10.3390/su10051515 · Cited by 70 · see citing works

Predicting Primary Energy Consumption Using Hybrid ARIMA and GA-SVR Based on EEMD Decomposition

Kao, Y.-S., Nawata, K., & Huang, C.-Y. (2020) · Mathematics · doi:10.3390/math8101722 · Cited by 38 · see citing works

Web, Artificial Intelligence and Network Applications

(2019) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-15035-8 · Cited by 29 · see citing works

Research on EA-Xgboost Hybrid Model for Building Energy Prediction

Wu, Y., & Wang, B. (2020) · Journal of Physics Conference Series · doi:10.1088/1742-6596/1518/1/012082 · Cited by 24 · see citing works

Energy Consumption Prediction Using Deep Learning Technique

Alanbar, M., Alfarraj, A., & Alghieth, M. (2020) · International Journal of Interactive Mobile Technologies (iJIM) · doi:10.3991/ijim.v14i10.14383 · Cited by 20 · see citing works

Dynamic turning force prediction and feature parameters extraction of machine tool based on ARMA and HHT

Zhang, B., & Zhao, C. (2019) · Proceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science · doi:10.1177/0954406219888954 · Cited by 9 · see citing works

Interpretable machine learning for building energy management: A state-of-the-art review

Chen, Z., Xiao, F., Guo, F., & Yan, J. (2023) · Advances in Applied Energy · doi:10.1016/j.adapen.2023.100123 · Cited by 357 · see citing works

Combining physical approaches with deep learning techniques for urban building energy modeling: A comprehensive review and future research prospects

Li, Z., Ma, J., Tan, Y., Guo, C., & Li, X. (2023) · Building and Environment · doi:10.1016/j.buildenv.2023.110960 · Cited by 60 · see citing works

Interpretable building energy performance prediction using XGBoost Quantile Regression

Uyar, S. G. K., Ozbay, B. K., & Dal, B. (2025) · Energy and Buildings · doi:10.1016/j.enbuild.2025.115815 · Cited by 48 · see citing works

Which product description phrases affect sales forecasting? An explainable AI framework by integrating WaveNet neural network models with multiple regression

Chen, S., Ke, S., Han, S., Gupta, S., & Sivarajah, U. (2023) · Decision Support Systems · doi:10.1016/j.dss.2023.114065 · Cited by 45 · see citing works

A Multivariate Time Series Analysis of Electrical Load Forecasting Based on a Hybrid Feature Selection Approach and Explainable Deep Learning

Yaprakdal, F., & Arısoy, M. V. (2023) · Applied Sciences · doi:10.3390/app132312946 · Cited by 45 · see citing works

Advanced Digital Tools for Data-Informed and Performance-Driven Design: A Review of Building Energy Consumption Forecasting Models Based on Machine Learning

di Stefano, A. G., Ruta, M. F., & Masera, G. (2023) · Applied Sciences · doi:10.3390/app132412981 · Cited by 45 · see citing works

Evolution of Building Energy Management Systems for greater sustainability through explainable artificial intelligence models

González‐Briones, A., Palomino-Sánchez, J., Vale, Z., Ramos, C., & Corchado, J. M. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.110324 · Cited by 17 · see citing works

Explainable AI and Multi-Agent Systems for Energy Management in IoT-Edge Environments: A State of the Art Review

Álvarez-López, C., González-Briones, A., & Li, T. (2026) · Electronics · doi:10.3390/electronics15020385 · Cited by 23 · see citing works

Explainable Approaches for Forecasting Building Electricity Consumption

Sakkas, N., Yfanti, S., Shah, P., Sakkas, N., Chaniotakis, C., Daskalakis, C., Barbu, E., & Domnich, M. (2023) · Energies · doi:10.3390/en16207210 · Cited by 14 · see citing works

Prediction of Sunspot Number with Hybrid Model Based on 1D-CNN, BiLSTM and Multi-Head Attention Mechanism

Chen, H., Liu, S., Yang, X., Zhang, X., Yang, J., & Fan, S. (2024) · Electronics · doi:10.3390/electronics13142804 · Cited by 10 · see citing works

Linking short-term electricity demand forecasting and explainable AI: A review for building energy applications

Ramos, D., Faria, P., & Vale, Z. (2026) · Applied Energy · doi:10.1016/j.apenergy.2026.127658 · Cited by 9 · see citing works

Grey-Box Method for Urban Building Energy Modelling: Advancements and Potentials

Guo, Y., Shi, J., Guo, T., Guo, F., Lu, F., & Su, L. (2024) · Energies · doi:10.3390/en17215463 · Cited by 8 · see citing works

Analyzing the impact of weather conditions on energy efficiency in residential buildings using machine learning techniques with explainable artificial intelligence

Iram, S., Shahzad, A., Farid, H. M. A., & Shakeel, H. M. (2025) · Advances in Building Energy Research · doi:10.1080/17512549.2025.2527675 · Cited by 7 · see citing works

Analysis of Business Customers’ Energy Consumption Data Registered by Trading Companies in Poland

Kowalska-Styczeń, A., Owczarek, T., Siwy, J., Sojda, A., & Wolny, M. (2022) · Energies · doi:10.3390/en15145129 · Cited by 6 · see citing works

Explainable Approaches for Forecasting Building Electricity ConsumptionPreprint

Sakkas, N., Yfanti, S., Shah, P., Sakkas, N., Chaniotakis, C., Daskalakis, C., Barbu, E., & Domnich, M. (2023) · Preprints.org · doi:10.20944/preprints202308.1230.v1 · Cited by 5 · see citing works

Sustainable Building Tool by Energy Baseline: Case Study

Mendoza, R. D. P. C., Rey-Hernández, J. M., Castrillón-Mendoza, L., & Martı́nez, F. J. R. (2024) · Applied Sciences · doi:10.3390/app14209403 · Cited by 4 · see citing works

ExplainerX: An Integrated and Explainable AI Framework for Nearly Zero-Energy Buildings

Kermiche, A., Rouzzi, C., & Bellatreche, L. (2023) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata59044.2023.10386148 · Cited by 4 · see citing works

Explainability Spectrum Analysis of Weather Sequences in Short-Term Load Forecasting

Kontogiannis, D., Bargiotas, D., Daskalopulu, A., & Tsoukalas, L. H. (2024) · Lecture notes in networks and systems · doi:10.1007/978-3-031-67426-6_6

Prediction Model of Energy Consumption Using Autoregressive Models and Differential Evolution

Vásquez, S. B. R., Boné-Andrade, M. F., & Zambrano, J. (2025) · Communications in computer and information science · doi:10.1007/978-3-031-83210-9_10

Explainable Artificial Intelligence Model for Electricity Usage Prediction in Nigeria

Alilu, G. E., Okorie, G. C., Ayoade, I. A., Obonna, U. O., Akinde, O. K., & Nwakanma, C. I. (2024) · IEEE Nigeria International Conference on Disruptive Technologies for Sustainable Development (NIGERCON) · doi:10.1109/nigercon62786.2024.10927260

Leveraging big data analytics to optimize user interaction in power marketing management information platforms

Kang, Y., Zhang, Y., Sun, W., Wu, R., Han, J., & Wei, Y. (2025) · Journal of Computational Methods in Sciences and Engineering · doi:10.1177/14727978251361549

Merging Mathematics with AI and IoT as a Lever for City Sustainability

Lucas, C., Lourenço, J., Paulo, J. B., & Morais, J. C. (2026) · Springer eBooks · doi:10.1007/978-3-031-99147-9_19

Explainable AI-driven energy prediction and optimization for cleaner intelligent weaving: A hybrid large language model and weaving graph method

Xu, K., Dai, N., Gan, P., Qiao, Y., & Hu, X. (2026) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2026.148286

Electric Energy Demand Forecasting with Explainable Time-series Modeling

Kim, J., & Cho, S. (2020) · IEEE ... International Conference on Data Mining workshops · doi:10.1109/icdmw51313.2020.00101 · Cited by 9 · see citing works

Energy Consumption Prediction Based on LightGBM Empowered With eXplainable Artificial Intelligence

Munir, S., Pradhan, M. R., Abbas, S., & Khan, M. A. (2024) · IEEE Access · doi:10.1109/access.2024.3418967 · Cited by 35 · see citing works

Evolution of Building Energy Management Systems for greater sustainability through explainable artificial intelligence models

González‐Briones, A., Palomino-Sánchez, J., Vale, Z., Ramos, C., & Corchado, J. M. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.110324 · Cited by 17 · see citing works

Enhancing Household Energy Consumption Predictions Through Explainable AI Frameworks

Bhandary, A., Dobariya, V., Yenduri, G., Jhaveri, R. H., Gochhait, S., & Benedetto, F. (2024) · IEEE Access · doi:10.1109/access.2024.3373552 · Cited by 48 · see citing works

Explainable AI for Interpretable and Model-Agnostic Energy Consumption Prediction

Nwakanma, C. I., Ahakonye, L. A. C., Njoku, J. N., Gyawali, P., & Srivastava, A. K. (2025) · IEEE Industry Applications Society Annual Meeting (IAS) · doi:10.1109/ias62731.2025.11061571 · Cited by 1 · see citing works