Demonstrating measure-correlate-predict algorithms for estimation of wind resources in central Finland
In this study, measure-correlate-predict (MCP) algorithms - Simple Linear Regression and Variance Ratio Methods - for predicting wind speed were studied. The MCP algorithms were successfully used to predict missing wind speeds at two sites in Jyväskylä and Viitasaari, respectively. These two algorithms used data from one of the site to predict missing wind speed data at the other site. The results obtained using the MCP methods were compared using metrics that showed the characteristics of the predicted data to be unbiased compared to measured data. From the data of this study, we also evaluated wind power density at both sites which categorized the local wind resources as poor since the determined wind power densities were less than 100 W/m2.
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Techno-economic pre-feasibility study of wind and solar electricity generating systems for households in Central Finland
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Distributed Resource Allocation for Energy Efficiency in OFDMA Multicell Networks with Wireless Power Transfer
Chang, Zheng; Wang, Zhongyu; Guo, Xijuan; Yang, Chungang; Han, Zhu; Ristaniemi, Tapani (Institute of Electrical and Electronics Engineers, 2019)In this paper, an energy-efficient resource allocation problem is investigated for the wireless power transfer (WPT)-enabled OFDMA multicell networks. In the considered system, multiple base stations (BSs) with a large ...
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