The rapid development of the Internet of Things (IoT) has brought a data explosion and a new set of challenges. It has been an emergency to construct a more robust and precise model to predict the electricity consumption data collected from the Internet of Things (IoT). Accurately forecasting the electricity consumption is a crucial technology for the planning of the energy resource which could lead to remarkable conservation of the building electricity consumption. This paper is focused on the electricity consumption forecasting of an office building with a small-scale dataset, and 117 daily electricity consumption of the building are involved in the dataset, among which 89 values are selected as the training dataset and the remaining 28 values as the testing dataset. The hybrid model ARIMA (autoregression integrated moving average)-SVR (support vector regression) is proposed to predict the electricity consumption with different prediction horizons ranging from 1 day to 28 days. The model performances are assessed by three evaluation indicators, respectively, are the mean squared error (MSE), the root mean square error (RMSE), and the mean absolute percentage error (MAPE). The proposed model ARIMA-SVR is compared with the other four models, respectively, are the ARIMA, ARIMA-GBR (gradient boosting regression), LSTM (long short-term memory), and GRU (gated recurrent unit) models. The experiment result shows that the ARIMA-SVR model has lower prediction errors when the prediction horizon is within 20 days, and the ARIMA model is better when the prediction horizon is in the interval of 20 to 28 days. The provided method ARIMA-SVR has higher flexibility, and it is a great choice for electricity consumption prediction with more accurate results.

Massive energy consumption data of buildings was generated with the development of information technology, and the real-time energy consumption data was transmitted to energy consumption monitoring system by the distributed wireless sensor network (WSN). Accurately predicting the energy consumption is of importance for energy manager to make advisable decision and achieve the energy conservation. In recent years, considerable attention has been gained on predicting energy use of buildings in China. More and more predictive models appeared in recent years, but it is still a hard work to construct an accurate model to predict the energy consumption due to the complexity of the influencing factors. In this paper, 40 weather factors were considered into the research as input variables, and the electricity of supermarket which was acquired by the energy monitoring system was taken as the target variable. With the aim to seek the optimal subset, three feature selection (FS) algorithms were involved in the study, respectively: stepwise, least angle regression (Lars), and Boruta algorithms. In addition, three machine learning methods that include random forest (RF) regression, gradient boosting regression (GBR), and support vector regression (SVR) algorithms were utilized in this paper and combined with three feature selection (FS) algorithms, totally are nine hybrid models aimed to explore an improved model to get a higher prediction performance. The results indicate that the FS algorithm Boruta has relatively better performance because it could work well both on RF and SVR algorithms, the machine learning method SVR could get higher accuracy on small dataset compared with the RF and GBR algorithms, and the hybrid model called SVR-Boruta was chosen to be the proposed model in this paper. What is more, four evaluate indicators were selected to verify the model performance respectively are the mean absolute error (MAE), the mean squared error(MSE), the root mean squared error (RMSE), and the R-squared (R2), and the experiment results further verified the superiority of the recommended methodology.

With the rapid development of the Internet of things (IoT) technology, the application of IoT has been expanded greatly, and the disadvantages of the traditional battery power supply have become increasingly prominent. The power supply mode limits the development of the concrete structural health monitoring network. And the application of magnetic resonance coupled wireless power transfer technology can solve the problem of power supply to sensors embedded in concrete. The corrected transmission efficiency considering the concrete conductivity is proposed which establishes the relationship between the electromagnetic field and the circuit model. And the field-circuit coupled model of asymmetric wireless power transfer system in concrete is developed. The effects of radial offset and axial dislocation on the transmission efficiency at different concrete conductivity are further analyzed. The relationship between the resonant frequency and the transmission efficiency in different concrete conductivity is analyzed, and an optimization scheme is proposed to improve the transmission efficiency. Finally, the experimental setups are established, and the theoretical analysis is verified. The conclusions cannot only break through the bottleneck of the scale of the concrete structural health monitoring network but also further releases the application potential of IoT.

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Achieving stable power transfer by merely relying on quasi-omnidirectional couplers is challenging. In this paper, we propose a quasi-omnidirectional wireless power transfer (QWPT) system with a novel curved-coil transmitter to achieve steady transmission performance. A single power source is used to drive the transmitter's current without using a phase and current control methodology. Power is transmitted to the receiver through magnetic resonant coupling at a distance of 50 mm. Moreover, an equivalent circuit model of the curved-coil system is derived and mathematically analyzed. The mutual inductance of the proposed QWPT system is evaluated through analysis and experiments. The experimental results for the resonant coupling system confirm the theoretical analysis of the performance of the curved-coil transmitter and quasi-omnidirectional power transfer.