Conference Paper

Smart Refrigeration Equipment based on IoT Technology for Reducing Power Consumption

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... In order to reduce the energy consumption of cooling storage in the past, a mechanical method of simply controlling the frequency of the compressor to reduce power consumption was used, but Kim [14] presented reported that aimed to reduce the power consumption of refrigeration equipment by using machine learning techniques based on data obtained from the IoT and consequently reduce the carbon energy footprint for food retailers. To implement this method, the temperature measured by connecting digital sensors was transmitted to a cloud server through a wireless network, and it was proven that optimal operating conditions could be created through the machine learning of cloud data. ...
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This study discusses methods for the sustainability of freezers used in frozen storage methods known as long-term food storage methods. Freezing preserves the quality of food for a long time. However, it is inevitable to use a freezer that uses a large amount of electricity to store food with this method. To maintain the quality of food, lower temperatures are required, and therefore more electrical energy must be used. In this study, machine learning was performed using data obtained through a freezer test, and an optimal inference model was obtained with this data. If the inference model is applied to the selection of freezer control parameters, it turns out that optimal food storage is possible using less electrical energy. In this paper, a method for obtaining a dataset for machine learning in a deep freezer and the process of performing SLP and MLP machine learning through the obtained dataset are described. In addition, a method for finding the optimal efficiency is presented by comparing the performances of the inference models obtained in each method. The application of such a development method can reduce electrical energy in the food manufacturing equipment related industry, and accordingly it will be possible to achieve carbon emission reductions.
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The mitigation of greenhouse gases in the agri-food sector depends on production and consumption patterns. This study focuses on the impact of urban gardening activities on food consumption and the carbon footprint. Changes in the food habits of citizens involved in urban agriculture activities in the city of Madrid were assessed over a five-year period using an online survey. The impact of habit change on the average carbon footprint from food consumption was assessed using a life-cycle approach. The results display a potential reduction of up to 205.1 kg CO2e/year per person (12.1%), which can mainly be achieved with a reduction in animal source foods. The results suggest that urban gardens could be used as social catalysts for pro-environmental behavior and greenhouse gas mitigation in urban areas.
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In this work, a regression method is implemented on a low-cost digital temperature sensor to improve the sensor’s accuracy; thus, following the EN12830 European standard. This standard defines that the maximum acceptable error regarding temperature monitoring devices should not exceed 1 °C for the refrigeration and freezer areas. The purpose of the proposed method is to improve the accuracy of a low-cost digital temperature sensor by correcting its nonlinear response using simple linear regression (SLR). In the experimental part of this study, the proposed method’s outcome (in a custom created dataset containing values taken from a refrigerator) is compared against the values taken from a sensor complying with the EN12830 standard. The experimental results confirmed that the proposed method reduced the mean absolute error (MAE) by 82% for the refrigeration area and 69% for the freezer area—resulting in the accuracy improvement of the low-cost digital temperature sensor. Moreover, it managed to achieve a lower generalization error on the test set when compared to three other machine learning algorithms (SVM, B-ELM, and OS-ELM).
Manufacturers expect the extra value of Industry 4.0 as the world is experiencing digital transformation. Studies have proved the potential of the Internet of Things (IoT) for reducing cost, improving efficiency, quality, and achieving data-oriented predictive maintenance services. Collecting a wide range of real-time data from products and the environment requires smart sensors, reliable communications, and seamless integration. IoT, as a critical Industry 4.0 enabler emerges smart home appliances for higher customer satisfaction, energy efficiency, personalisation, and advanced Big data analytics. However, established factories with limited resources are facing challenges to change the longstanding production lines and meet customer’s requirements. This study aims to fulfil the gaps by transforming conventional home appliances to IoT-enabled smart systems with the ability to integrate into a smart home system. An industry-led case study demonstrates how to turn conventional appliances to smart products and systems (SPS) by utilising the state-of-the-art Industry 4.0 technologies.
Urban diets and nutrition: Trends, challenges and opportunities for policy action
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The role of refrigeration in the global economy-29. informatory note on refrigeration technologies
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The role of refrigeration in the global economy-29. informatory note on refrigeration technologies
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Prediction of the energy consumption of a supermarket refrigeration system
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