Estimating Adaptive Setpoint Temperatures Using Weather Stations

Bienvenido-Huertas, David; Rubio-Bellido, Carlos; Ordóñez, Juan Luis Pérez; Martínez-Abella, Fernando

doi:10.3390/en12071197

Cited by 14 publications

(6 citation statements)

References 51 publications

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“…The design of buildings should logically be adapted to the lowest energy consumption levels, but in most cases, it is necessary to focus on existing buildings [2]. Therefore, the reduction of both energy consumption and CO 2 emissions from buildings is one of society's main targets today [3]. In Spain, there is a climatic classification according to the technical code of the building that contemplates these issues, which has been mandatory since 2006 [4].…”

Section: Introductionmentioning

confidence: 99%

Benchmarking Energy Use at University of Almeria (Spain)

2020

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Several factors impact the energy use of university campus buildings. This study aims to benchmark the energy use in universities with Mediterranean climates. The University of Almeria campus was used as a case study, and different types of buildings were analyzed. The second goal was to model the electricity consumption and determinate which parameter correlate strongly with energy use. Macro-scale energy consumption data during a period of seven years were gathered alongside cross-sectional buildings information. Eight years of daily outdoor temperature data were recorded and stored for every half hour. This dataset was eventually used to calculate heating and cooling degree-days. The weather factor was recognized as the variable with the greatest impact on campus energy consumption, and as the coefficient indicated a strong correlation, a linear regression model was established to forecast future energy use. A threshold of 8 GWh has been estimated as the energy consumption limit to be achieved despite the growth of the university. Finally, it is based on the results to inform the recommendations for decision making in order to act effectively to optimize and achieve a return on investment.

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Section: Introductionmentioning

confidence: 99%

Benchmarking Energy Use at University of Almeria (Spain)

2020

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“…This method assumes a linear relationship between the dependent variable and the independent variables. The multiple linear regression model can be expressed as follows [32]:…”

Section: Methodology 221 Multiple Linear Regressionmentioning

confidence: 99%

A Fast Screening Method of Key Parameters from Coal for Carbon Emission Enterprises

Lu,

Chen,

Song

et al. 2023

Energies

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During the process of determining carbon emissions from coal using the emission factor method, third-party organizations in China are responsible for verifying the accuracy of the carbon emission data. However, these verifiers face challenges in efficiently handling large quantities of data. Therefore, this study proposed a fast screening method that utilizes multiple linear regression (MLR), in combination with the stepwise backward regression method, to identify problematic carbon emission data for the lower calorific value (LCV) and carbon content (C) of coal. The results demonstrated the effectiveness of the proposed method. The regression models for LCV and C exhibited high R-squared (R2) values of 0.9784 and 0.9762, respectively, and the root mean square error (RMSE) values of the validation set were 0.32 MJ/kg and 0.80% for LCV and C, respectively, indicating strong predictive capabilities. By analyzing the obtained results, the study established the optional error threshold interval for the LCV and C of coal as 2RMSE–3RMSE. This interval can be utilized as a reliable criterion for judging the quality and reliability of carbon emission data during the verification process. Overall, the proposed screening method can serve as a valuable tool for verifiers in assessing the quality and reliability of carbon emission data in various regions.

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“…[ [72][73][74][75][76][77][78][79][80][81][82][83][84][85][86][87][88] [89] Data collection from Sensor node is proposed. The station is proposed to be Automatic Weather Station-AWS where the system sends the data to remote user.…”

Section: Data Collectionmentioning

confidence: 99%

LoRa based intelligent soil and weather condition monitoring with internet of things for precision agriculture in smart cities

et al. 2022

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Urbanization is expected to hold about 50% of the world population by 2050 and there will be stress on available resources including food and freshwater. Further, inefficient utilization of irrigation water possesses additional challenges and it is predicted that by 2050, India will face shortage of freshwater resources. Urban agriculture and Precision Agriculture are the possible solutions in smart cities for the predicted challenges providing food security and safety, sustainable life in cities. Internet of Things (IoT) and Machine Learning (ML) are the possible solutions to smart cities' farming. Agricultural activities such as irrigation of plants, rain and drought conditions monitoring supported by IoT and ML succour farmers to righteous decision on agriculture activities. A detailed study is carried out on the role, opportunities, and different aspects of smart cities, urban farming, communication technologies, IoT and ML with respect to agriculture. The article presents the design of intelligent irrigation system based on soil and weather conditions. The soil and weather parameters are selected through various research articles in Agriculture 4.0 and ML. The article also juxtapositions the designed weather station with the various patents developed. The system developed in this article provides a cost‐effective and state‐of‐the‐art solution to local weather monitoring.

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Estimating Adaptive Setpoint Temperatures Using Weather Stations

Cited by 14 publications

References 51 publications

Benchmarking Energy Use at University of Almeria (Spain)

Benchmarking Energy Use at University of Almeria (Spain)

A Fast Screening Method of Key Parameters from Coal for Carbon Emission Enterprises

LoRa based intelligent soil and weather condition monitoring with internet of things for precision agriculture in smart cities

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