Harvested Energy Prediction Schemes for Wireless Sensor Networks: Performance Evaluation and Enhancements

Abstract

We review harvested energy prediction schemes to be used in wireless sensor networks and explore the relative merits of landmark solutions. We propose enhancements to the well-known Profile-Energy (Pro-Energy) model, the so-called Improved Profile-Energy (IPro-Energy), and compare its performance with Accurate Solar Irradiance Prediction Model (ASIM), Pro-Energy, and Weather Conditioned Moving Average (WCMA). The performance metrics considered are the prediction accuracy and the execution time which measure the implementation complexity. In addition, the effectiveness of the considered models, when integrated in an energy management scheme, is also investigated in terms of the achieved throughput and the energy consumption. Both solar irradiance and wind power datasets are used for the evaluation study. Our results indicate that the proposed IPro-Energy scheme outperforms the other candidate models in terms of the prediction accuracy achieved by up to 78% for short term predictions and 50% for medium term prediction horizons. For long term predictions, its prediction accuracy is comparable to the Pro-Energy model but outperforms the other models by up to 64%. In addition, the IPro scheme is able to achieve the highest throughput when integrated in the developed energy management scheme. Finally, the ASIM scheme reports the smallest implementation complexity.

Prediction of Harvested Energy for Wireless Sensor Node

“…The authors in [12] have presented an improved version of Pro-Energy (IPro-Energy), which introduces a "smarting factor" S to compensate for interday weather changes. The "smarting factor" is calculated as…”

A Hybrid Prediction Model for Solar Radiation Based on Long Short-Term Memory, Empirical Mode Decomposition, and Solar Profiles for Energy Harvesting Wireless Sensor Networks

“…In this section, we summarize the state-of-the-art prediction models. As we have mentioned earlier, solar radiation prediction models have statistical, stochastic, and machine learning methods [5]. Statistical models include the classic exponential weighted moving average (EWMA) [6], the weather conditioned moving average (WCMA) [7], and the profile-energy (Pro-Energy) model [15].…”

“…Since the weather forecasting information is not always available, our research focuses on prediction approaches without weather information. Under this category, the solar radiation prediction models are categorized into three major classes: statistical, stochastic, and machine learning methods [5]. Statistical models are based on statistical information, such as standard deviation, variance, mean, and moving average, which includes the classic exponential weighted moving average (EWMA) [6], the weather conditioned moving average (WCMA) [7] and their improvements.…”

“…Machine learning prediction uses machine learning-based techniques, such as neural networks (NN) [8] and fuzzy logic (FL) [9], to build models to handle time series prediction. Machine learning prediction schemes are shown to outperform the traditional models by achieving increased accuracy but with a more substantial computational burden [5]. However, neural networks have two obvious weaknesses, i.e., slow convergence and the presence of local optima.…”

“…where H represents the amount of energy collected in the previous time slot and E MS is the energy observed in time slot n on the most similar day. To determine the level of similarity from the previous few days to the day of D, the mean absolute error (MAE) of each stored day of the K previous time slots up to the current time slot is calculated in Pro-Energy as in Equation (5). Calculate the mean absolute error (MAE) with each stored profile and select the profile with the smallest MAE.…”