Radio Frequency (RF) energy harvesting has been used to power wireless and low-powered devices. However, RF energy harvesting has limitations in terms of the amount of power that can be collected based on signal availability. Hence, energy prediction is essential to improve energy harvesting circuits' performance. Previous research has mainly focused on improving power harvesting policies or theoretically estimating the harvested energy. Very few works have considered the prediction of the RF signal as time series data using real RF measurements. Moreover, challenges such as the power consumed by the circuit's harvesting decisions and the impact of outliers on the model performance haven't been addressed yet. This paper presents a complete pipeline for developing the best predictive model for RF energy in cellular frequency bands. Real-time measurements are taken in different frequency bands using software-defined radio technology. We use four artificial intelligence techniques to model the RF energy signal. Additionally, we propose an optimized model with an enhanced loss function, which makes the model more resilient to anomalies, saving computational power and time consumed in cleaning the data. The four algorithms are investigated, and their prediction accuracies are compared. The average power of a period of 5 min is accurately forecasted. Numerical results in the 1960 MHz band show that long short-term memory has the best performance, followed by the DeepAR algorithm with prediction accuracies of 95.76% and 95.02%, respectively. Moreover, the proposed optimized model showed a 32.2% lower prediction error than the traditional models.
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