Integrating real-time data with web data for efficient energy harvesting systems

Since eco-friendly green energy is currently being emphasized, multi-source energy harvesting technology attracts great attention not only to industry but also academia. In this paper, we propose a novel approach for integrating real-time and web data for efficient energy harvesting systems. The real-time and web data integration occurs on an intelligent cloud system to minimize the load on the harvesting device. The real-time data are extracted and corrected in case of errors; specifically, error correction is performed by identifying outliers based on the average slope of data. Furthermore, the erroneous data are smoothed through the modified moving average filter. Additionally, web data are acquired from official centers and trimmed based on the location and time of measurement. After the processing, all of these data are integrated using a weighted average. The validity of the data integration is evaluated by comparing correlation coefficients for the original and integrated sets of data. In addition, an advanced design of efficient energy harvesting prototype is introduced and implemented. We expect that integrating data reflects the overall trend of ambient circumstances for efficient energy harvesting systems.