Energy is one of the most critical constraints for sensor network applications. In this paper, we exploit the localized prediction paradigm for power-efficient object tracking sensor network. Localized prediction consists of a localized network architecture and a prediction mechanism called dual prediction, which achieve power savings by allowing most of the sensor nodes stay in sleep mode and ...

Renewable energy production has been increasing at a tremendous rate in the past decades. This increase led to various benefits such as low cost of and making independent fossil fuels. However, order fully reap renewable produce an optimum manner, it is essential that we forecast production. Historically deep learning-based techniques have successful accurately forecasting solar In this paper d...

Energy is one of the most critical constraints for sensor network applications. In this paper, we exploit the localized prediction paradigm for power-efficient object tracking sensor network. Localized prediction consists of a localized network architecture and a prediction mechanism called dual prediction, which achieve power savings by allowing most of the sensor nodes stay in sleep mode and ...

In this paper, we present a prediction based closed loop fast transmit power control algorithm on the downlink in wideband CDMA. We use an order predictor to predict the received signal power for comparison with the target received power. We evaluate the performance of the prediction based closed loop power control (CLPC) as a function of mobile speed and propagation and processing delays. We s...

Wind power prediction of wind farm plays a decisive role in stable electric power system operation.The BP neural network’s basic principle was introduced, and the numerical weather prediction (NWP) data and power data of wind farm as the training data of BP neural network was selected and trained; a linear regression model about the sample prediction error was presented, which considers the cou...

Prediction of solar irradiance has great significance to photovoltaic power forecasting and the scheduling plan of power generation. Aim at unsatisfactory prediction accuracy of traditional forecasting methods, this paper presents an approach to predict solar irradiance of photovoltaic power station based on wavelet decomposition and extreme learning machine. With historical irradiance sequence...