{"total":24,"rows":[{"id":"2210.10391","title":"Machine Learning for a Sustainable Energy Future","created":"2022-10-19","cited_by_count":14,"area":null},{"id":"2301.13036","title":"Federated Learning for Water Consumption Forecasting in Smart Cities","created":"2023-01-30","cited_by_count":7,"area":null},{"id":"2306.09129","title":"Deep Learning for Energy Time-Series Analysis and Forecasting","created":"2023-06-15","cited_by_count":5,"area":null},{"id":"2111.09248","title":"Privacy-preserving Federated Learning for Residential Short Term Load Forecasting","created":"2021-11-17","cited_by_count":4,"area":null},{"id":"2210.13108","title":"Heat Demand Forecasting with Multi-Resolutional Representation of Heterogeneous Temporal Ensemble","created":"2022-10-24","cited_by_count":2,"area":null},{"id":"2302.05033","title":"Short-Term Aggregated Residential Load Forecasting using BiLSTM and CNN-BiLSTM","created":"2023-02-10","cited_by_count":2,"area":null},{"id":"2408.17366","title":"Leveraging Graph Neural Networks to Forecast Electricity Consumption","created":"2024-08-30","cited_by_count":2,"area":null},{"id":"2212.04069","title":"Reinforcement Learning for Resilient Power Grids","created":"2022-12-08","cited_by_count":1,"area":null},{"id":"2212.08757","title":"Short-term Prediction of Household Electricity Consumption Using Customized LSTM and GRU Models","created":"2022-12-16","cited_by_count":1,"area":null},{"id":"2304.03343","title":"Spintronic Physical Reservoir for Autonomous Prediction and Long-Term Household Energy Load Forecasting","created":"2023-04-06","cited_by_count":1,"area":null},{"id":"2304.07832","title":"An Interpretable Approach to Load Profile Forecasting in Power Grids using Galerkin-Approximated Koopman Pseudospectra","created":"2023-04-16","cited_by_count":1,"area":null},{"id":"2507.03690","title":"Graph Neural Networks for Electricity Load Forecasting","created":"2025-07-04","cited_by_count":1,"area":null},{"id":"2602.05430","title":"Day-Ahead Electricity Price Forecasting for Volatile Markets Using Foundation Models with Regularization Strategy","created":"2026-02-05","cited_by_count":1,"area":null},{"id":"2102.12970","title":"A microservice-based framework for exploring data selection in cross-building knowledge transfer","created":"2021-02-23","cited_by_count":0,"area":null},{"id":"2207.02589","title":"Cascaded Deep Hybrid Models for Multistep Household Energy Consumption Forecasting","created":"2022-07-06","cited_by_count":0,"area":null},{"id":"2208.07643","title":"A Review of the Convergence of 5G/6G Architecture and Deep Learning","created":"2022-08-16","cited_by_count":0,"area":null},{"id":"2302.10889","title":"An Asymmetric Loss with Anomaly Detection LSTM Framework for Power Consumption Prediction","created":"2023-02-05","cited_by_count":0,"area":null},{"id":"2307.16149","title":"A Novel DDPM-based Ensemble Approach for Energy Theft Detection in Smart Grids","created":"2023-07-30","cited_by_count":0,"area":null},{"id":"2406.00596","title":"Multi-variable Adversarial Time-Series Forecast Model","created":"2024-06-02","cited_by_count":0,"area":null},{"id":"2511.10680","title":"LAD-BNet: Lag-Aware Dual-Branch Networks for Real-Time Energy Forecasting on Edge Devices","created":"2025-11-11","cited_by_count":0,"area":null},{"id":"2512.03656","title":"Cyclical Temporal Encoding and Hybrid Deep Ensembles for Multistep Energy Forecasting","created":"2025-12-03","cited_by_count":0,"area":null},{"id":"2603.25370","title":"A Distribution-to-Distribution Neural Probabilistic Forecasting Framework for Dynamical Systems","created":"2026-03-26","cited_by_count":0,"area":null},{"id":"2605.16250","title":"A Generative AI Framework for Intelligent Utility Billing CO 2 Analytics and Sustainable Resource Optimisation","created":"2026-05-15","cited_by_count":0,"area":null},{"id":"2609.19695","title":"Opinion Dynamics-based Coalition Formation for Federated Learning in Heterogeneous IoT Systems","created":"2026-09-17","cited_by_count":0,"area":null}],"next_cursor":null}