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Data-driven pre-training framework for reinforcement learning of air-source heat pump (ASHP) systems based on historical data in off...
The framework integrates data-driven models based on real-world historical data for load forecasting, equipment energy consumption, and heat transfer. A...
文献获取A DQN-Based Edge Offloading Method for Smart City Pollution Control
Initially, N-DEO employs neural hierarchical interpolation for time series forecasting (N-HITS) to forecast pollution control service requests. Afterwar...
Workflow Task Offloading Upon MEC: ANovel Mobility-Aware andClustering-Based Approach
for forecasting future user locations, a clustering-based multi-workflow merging algorithm for identifying redundant tasksand a Lyapunov optimization-ba...
Joint Optimization of Resource Allocation and Topology Formation for Hierarchical Federated Learning in Smart Grids
Hierarchical Federated Learning (HFL) presents a promising approach for addressing communication challenges inherent in traditional FL methodologies, pa...
Machine Learning-Driven Optimization of MTPA in IPMSM Drives for Electric Vehicles
This research presented in this paper outlines a pioneering optimization framework concentrated on perfecting the Maximum Torque per Ampere (MTPA) contr...
A novel two-stage reinforcement learning framework for sustainable building energy management systems
load forecasting accuracy.DQN enhances charging/discharging strategies for energy management.Reduces building energy costs by 10%, increases new energy ...
Load-Balanced Dynamic SFC Migration Based on Resource Demand Prediction
In network function virtualization, the resource demand of network services changes with network traffic. SFC migration has emerged as an effective tech...
Optimization of a photovoltaic-battery system using deep reinforcement learning and load forecasting
Home Energy Management Systems (HEMS) are increasingly relevant for demand-side management at the residential level by collecting data (energy, weather,...
