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Development of a Novel Spherical Light-Based Positioning Sensor in Solar Tracking
No full textTracking of the sun, which increases the efficiency of solar energy production systems, has shown considerable development in recent years. This development has been achieved by custom-positioned light sensors, image cameras, sensorless chronological systems and intelligent controller supported systems or by synergetic use of these systems. This study contributes to this research area with a novel spherical-based sensor which measures spherical light source emittance and localizes the light source. This sensor was built by using miniature light sensors placed on a spherical shaped three-dimensional printed body with data acquisition electronic circuitry. Besides the developed sensor data acquisition embedded software, preprocessing and filtering processes were conducted on these measured data. In the study, the outputs of Moving Average, Savitzky-Golay, and Median filters were used for the localization of the light source. The center of gravity for each filter used was determined as a point, and the location of the light source was determined. The spherical sensor system obtained by this study is applicable for various solar tracking methods. The approach of the study also shows that this measurement system is applicable for obtaining the position of local light sources such as the ones placed on mobile or cooperative robots.Chemistry, Analytical || Engineering, Electrical & Electronic || Instruments & Instrumentatio
Political Ecology of Austerity: Crisis, Social Movements, and the Environment
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Renewable energy management in smart home environment via forecast embedded scheduling based on Recurrent Trend Predictive Neural Network
No full textSmart home energy management systems help the distribution grid operate more efficiently and reliably, and enable effective penetration of distributed renewable energy sources. These systems rely on robust forecasting, optimization, and control/scheduling algorithms that can handle the uncertain nature of demand and renew-able generation. This paper proposes an advanced ML algorithm, called Recurrent Trend Predictive Neural Network based Forecast Embedded Scheduling (rTPNN-FES), to provide efficient residential demand control. rTPNN-FES is a novel neural network architecture that simultaneously forecasts renewable energy generation and schedules household appliances. By its embedded structure, rTPNN-FES eliminates the utilization of separate algorithms for forecasting and scheduling and generates a schedule that is robust against forecasting errors. This paper also evaluates the performance of the proposed algorithm for an IoT-enabled smart home. The evaluation results reveal that rTPNN-FES provides near-optimal scheduling 37.5 times faster than the optimization while outperforming state-of-the-art forecasting techniques.Energy & Fuels || Engineering, Chemica
Covid-19 pandemisi sürecinde kurum içi iletişim çalışmalarının kurumsal bağlılığa etkisi: Ege Bölgesi'nde bir enerji şirketine yönelik araştırma
Get PDFA predictive controller for efficient operations of HVAC systems in commercial buildings: Algorithm development and field application
Get PDFInternet of things application in an agricultural food supply chain to reduce food loss and waste
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