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Prediction of building energy consumption using mathematical modeling is crucial for improving the efficiency of building energy utilization, assisting in building energy consumption planning and scheduling, and further achieving the goal of energy conservation and emission reduction. In consideration of the non-linear and non-smooth characteristics of building energy consumption time series data, a short-term, hybrid building energy consumption prediction model combining variational mode decomposition (VMD), a simulated annealing (SA) algorithm, and a deep belief network (DBN) is proposed in this study. In the proposed VMD-SA-DBN model, the VMD algorithm decomposes the time series into different modes to reduce the fluctuation of the data. The SA-DBN prediction model is built for each mode separately, and the DBN network structure parameters are optimized by the SA algorithm. The prediction results of each model are aggregated and reconstructed to obtain the final prediction output. The validity and prediction performance of the proposed model is evaluated on a publicly available dataset, and the results show that the proposed new model significantly improves the accuracy and stability of building energy consumption prediction compared with several typical machine learning methods. The mean absolute percent error (MAPE) of the VMD-SA-DBN model is 63.7%, 65.5%, 46.83%, 64.82%, 44.1%, 36.3%, and 28.3% lower than that of the long short-term memory (LSTM), gated recurrent unit (GRU), VMD-LSTM, VMD-GRU, DBN, SA-DBN, and VMD-DBN models, respectively. The results will help managers formulate more-favorable low-energy emission reduction plans and improve building energy efficiency. [ABSTRACT FROM AUTHOR]

Mobile Agent (MA) technology brings many benefits into Wireless Sensor Networks (WSNs), such as saving network bandwidth and enabling energy efficient mechanisms for collecting sensor data. Nowadays, itinerary planning for MAs is one of the most important features of the WSN. However, the way in which all dispatched MAs are routed inside the sensor networks must be intelligently planned to reduce energy consumption and improve information accuracy. There have been many research efforts designing itinerary planning algorithms to deploy multiple MAs in a given sensor network, where routes are generated so that MAs can follow different routes to collect data from sensor nodes efficiently and effectively. This paper proposes a new energy efficient Graph-based Static Mutli-Mobile Agent Itinerary Planning approach (GSMIP). GSMIP applies Directed Acyclic Graph (DAG) related techniques and divide sensor nodes into different groups based on the routes defined by MAs itineraries. MAs follow the predefined routes and only collect data from the groups they are responsible for. The experimental findings demonstrate the effectiveness and superiority of the proposed approach compared to the existing approaches in terms of energy consumption and task delay (time). [ABSTRACT FROM AUTHOR]