Optimization of Greenhouse Thermal Screens for Maximized Energy Conservation

In this work, we proposed a Building Energy Simulation (BES) dynamic climatic model of greenhouses by utilizing Transient System Simulation (TRNSYS 18) software to study the effect of use of different thermal screen materials and control strategies of thermal screens on heat energy requirement of greenhouses. Thermal properties of the most common greenhouse thermal screens were measured and used in the BES model. Nash-Sutcliffe efficiency coefficients of 0.84 and 0.78 showed good agreement between the computed and experimental results, thus the proposed model appears to be appropriate for performing greenhouse thermal simulations. The proposed model was used to evaluate the effects of different thermal screens including; Polyester, Luxous, Tempa, and Multi-layers, as well as to evaluate control strategies of greenhouse thermal screens, subjected to Daegu city, (latitude 35.53 °N, longitude 128.36 °E) South Korea winter season weather conditions. Obtained results show that the heating requirement of greenhouses with multi-layer night thermal screens was 20%, 5.4%, and 13.5%, less than the Polyester, Luxous, and Tempa screens respectively. Thus, our experiments confirm that the use of multi-layered thermal screen can reduce greenhouse heat energy requirement. Furthermore, screen-control with outside solar radiation at an optimum setpoint of 60 W·m−2 significantly influences the greenhouse’s energy conservation capacity, as it exhibited 699.5 MJ · m−2, the least energy demand of all strategies tested. Moreover, the proposed model allows dynamic simulation of greenhouse systems and enables researchers and farmers to evaluate different screens and screen control strategies that suit their investment capabilities and local weather conditions.

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Development of a Building Energy Simulation Model for Control of Multi-Span Greenhouse Microclimate

Agronomy ◽

10.3390/agronomy10091236 ◽

2020 ◽

Vol 10 (9) ◽

pp. 1236

Author(s):

Adnan Rasheed ◽

Cheul Soon Kwak ◽

Wook Ho Na ◽

Jong Won Lee ◽

Hyeon Tae Kim ◽

...

Keyword(s):

Simulation Model ◽

Natural Ventilation ◽

Winter Season ◽

Weather Conditions ◽

Building Energy ◽

Energy Simulation ◽

Building Energy Simulation ◽

Proposed Model ◽

Heating Loads ◽

Maximum Heating

In this study, we propose a building energy simulation model of a multi-span greenhouse using a transient system simulation program to simulate greenhouse microenvironments. The proposed model allows daily and seasonal control of screens, roof vents, and heating setpoints according to crop needs. The proposed model was used to investigate the effect of different thermal screens, natural ventilation, and heating setpoint controls on annual and maximum heating loads of a greenhouse. The experiments and winter season weather conditions of greenhouses in Taean Gun (latitude 36.88° N, longitude 126.24° E, elevation 45 m) Chungcheongnam-do, South Korea was used for validation of our model. Nash–Sutcliffe efficiency coefficients of 0.87 and 0.71 showed good correlation between the computed and experimental results; thus, the proposed model is appropriate for performing greenhouse thermal simulations. The results showed that the heating loads of the triple-layered screen were 70% and 40% lower than that of the single-screen and double-screen greenhouses, respectively. Moreover, the maximum heating loads without a screen and for single-, double-, and the triple-layered screens were 0.65, 0.46, 0.41, and 0.34 MJ m−2, respectively. The analysis of different screens showed that Ph-77 (shading screen) combined with Ph-super (thermal screen) had the least heating requirements. The heating setpoint analysis predicted that using the designed day- and nighttime heating control setpoints can result in 3%, 15%, 14%, 15%, and 40% less heating load than when using the fixed value temperature control for November, December, January, February, and March, respectively.

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Control Strategies for Daylight and Artificial Lighting in Office Buildings—A Bibliometrically Assisted Review

Energies ◽

10.3390/en14133852 ◽

2021 ◽

Vol 14 (13) ◽

pp. 3852

Author(s):

Daniel Plörer ◽

Sascha Hammes ◽

Martin Hauer ◽

Vincent van Karsbergen ◽

Rainer Pfluger

Keyword(s):

Energy Efficiency ◽

Energy Demand ◽

Control Strategies ◽

Significant Proportion ◽

Decisive Role ◽

Future Trend ◽

Office Buildings ◽

Total Energy Consumption ◽

Integral Control ◽

Artificial Lighting

A significant proportion of the total energy consumption in office buildings is attributable to lighting. Enhancements in energy efficiency are currently achieved through strategies to reduce artificial lighting by intelligent daylight utilization. Control strategies in the field of daylighting and artificial lighting are mostly rule-based and focus either on comfort aspects or energy objectives. This paper aims to provide an overview of published scientific literature on enhanced control strategies, in which new control approaches are critically analysed regarding the fulfilment of energy efficiency targets and comfort criteria simultaneously. For this purpose, subject-specific review articles from the period between 2015 and 2020 and their research sources from as far back as 1978 are analysed. Results show clearly that building controls increasingly need to address multiple trades to achieve a maximum improvement in user comfort and energy efficiency. User acceptance can be highlighted as a decisive factor in achieving targeted system efficiencies, which are highly determined by the ability of active user interaction in the automatic control system. The future trend is moving towards decentralized control concepts including appropriate occupancy detection and space zoning. Simulation-based controls and learning systems are identified as appropriate methods that can play a decisive role in reducing building energy demand through integral control concepts.

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Overheating Risk and Energy Demand of Nordic Old and New Apartment Buildings during Average and Extreme Weather Conditions under a Changing Climate

Applied Sciences ◽

10.3390/app11093972 ◽

2021 ◽

Vol 11 (9) ◽

pp. 3972

Author(s):

Azin Velashjerdi Farahani ◽

Juha Jokisalo ◽

Natalia Korhonen ◽

Kirsti Jylhä ◽

Kimmo Ruosteenoja ◽

...

Keyword(s):

Climate Change ◽

Energy Demand ◽

Energy Use ◽

Residential Buildings ◽

Weather Conditions ◽

Extreme Weather ◽

Living Room ◽

Extra Energy ◽

Cooling Unit ◽

Indoor Conditions

The global average air temperature is increasing as a manifestation of climate change and more intense and frequent heatwaves are expected to be associated with this rise worldwide, including northern Europe. Summertime indoor conditions in residential buildings and the health of occupants are influenced by climate change, particularly if no mechanical cooling is used. The energy use of buildings contributes to climate change through greenhouse gas emissions. It is, therefore, necessary to analyze the effects of climate change on the overheating risk and energy demand of residential buildings and to assess the efficiency of various measures to alleviate the overheating. In this study, simulations of dynamic energy and indoor conditions in a new and an old apartment building are performed using two climate scenarios for southern Finland, one for average and the other for extreme weather conditions in 2050. The evaluated measures against overheating included orientations, blinds, site shading, window properties, openable windows, the split cooling unit, and the ventilation cooling and ventilation boost. In both buildings, the overheating risk is high in the current and projected future average climate and, in particular, during exceptionally hot summers. The indoor conditions are occasionally even injurious for the health of occupants. The openable windows and ventilation cooling with ventilation boost were effective in improving the indoor conditions, during both current and future average and extreme weather conditions. However, the split cooling unit installed in the living room was the only studied solution able to completely prevent overheating in all the spaces with a fairly small amount of extra energy usage.

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Usage of the Pareto Fronts as a Tool to Select Data in the Forecasting Process—A Short-Term Electric Energy Demand Forecasting Case

Energies ◽

10.3390/en14113204 ◽

2021 ◽

Vol 14 (11) ◽

pp. 3204

Author(s):

Michał Sabat ◽

Dariusz Baczyński

Keyword(s):

Energy Demand ◽

Demand Forecasting ◽

Electric Energy ◽

Forecasting Model ◽

Short Term ◽

Complex Evaluation ◽

Proposed Model ◽

Energy Demand Forecasting ◽

Pareto Fronts ◽

Naive Approach

Transmission, distribution, and micro-grid system operators are struggling with the increasing number of renewables and the changing nature of energy demand. This necessitates the use of prognostic methods based on ever shorter time series. This study depicted an attempt to develop an appropriate method by introducing a novel forecasting model based on the idea to use the Pareto fronts as a tool to select data in the forecasting process. The proposed model was implemented to forecast short-term electric energy demand in Poland using historical hourly demand values from Polish TSO. The study rather intended on implementing the range of different approaches—scenarios of Pareto fronts usage than on a complex evaluation of the obtained results. However, performance of proposed models was compared with a few benchmark forecasting models, including naïve approach, SARIMAX, kNN, and regression. For two scenarios, it has outperformed all other models by minimum 7.7%.

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Prediction of Pest Insect Appearance Using Sensors and Machine Learning

Sensors ◽

10.3390/s21144846 ◽

2021 ◽

Vol 21 (14) ◽

pp. 4846

Author(s):

Dušan Marković ◽

Dejan Vujičić ◽

Snežana Tanasković ◽

Borislav Đorđević ◽

Siniša Ranđić ◽

...

Keyword(s):

Machine Learning ◽

Relative Humidity ◽

Weather Conditions ◽

Daily Basis ◽

Machine Learning Algorithms ◽

Lower Percentage ◽

Timely Manner ◽

Proposed Model ◽

Set Up ◽

Accuracy Of Prediction

The appearance of pest insects can lead to a loss in yield if farmers do not respond in a timely manner to suppress their spread. Occurrences and numbers of insects can be monitored through insect traps, which include their permanent touring and checking of their condition. Another more efficient way is to set up sensor devices with a camera at the traps that will photograph the traps and forward the images to the Internet, where the pest insect’s appearance will be predicted by image analysis. Weather conditions, temperature and relative humidity are the parameters that affect the appearance of some pests, such as Helicoverpa armigera. This paper presents a model of machine learning that can predict the appearance of insects during a season on a daily basis, taking into account the air temperature and relative humidity. Several machine learning algorithms for classification were applied and their accuracy for the prediction of insect occurrence was presented (up to 76.5%). Since the data used for testing were given in chronological order according to the days when the measurement was performed, the existing model was expanded to take into account the periods of three and five days. The extended method showed better accuracy of prediction and a lower percentage of false detections. In the case of a period of five days, the accuracy of the affected detections was 86.3%, while the percentage of false detections was 11%. The proposed model of machine learning can help farmers to detect the occurrence of pests and save the time and resources needed to check the fields.

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Energy Performance Assessment of Innovative Building Solutions Coming from Construction and Demolition Waste Materials

Materials ◽

10.3390/ma14051226 ◽

2021 ◽

Vol 14 (5) ◽

pp. 1226

Author(s):

Beatriz Fraga-De Cal ◽

Antonio Garrido-Marijuan ◽

Olaia Eguiarte ◽

Beñat Arregi ◽

Ander Romero-Amorrortu ◽

...

Keyword(s):

Thermal Insulation ◽

Energy Demand ◽

Energy Savings ◽

Flow Analysis ◽

Weather Conditions ◽

Energy Performance ◽

Construction And Demolition Waste ◽

The European Union ◽

Demolition Waste ◽

The Impact

Prefabricated solutions incorporating thermal insulation are increasingly adopted as an energy conservation measure for building renovation. The InnoWEE European project developed three technologies from Construction and Demolition Waste (CDW) materials through a manufacturing process that supports the circular economy strategy of the European Union. Two of them consisted of geopolymer panels incorporated into an External Thermal Insulation Composite System (ETICS) and a ventilated façade. This study evaluates their thermal performance by means of monitoring data from three pilot case studies in Greece, Italy, and Romania, and calibrated building simulation models enabling the reliable prediction of energy savings in different climates and use scenarios. Results showed a reduction in energy demand for all demo buildings, with annual energy savings up to 25% after placing the novel insulation solutions. However, savings are highly dependent on weather conditions since the panels affect cooling and heating loads differently. Finally, a parametric assessment is performed to assess the impact of insulation thickness through an energy performance prediction and a cash flow analysis.

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Predicting Aedes aegypti infestation using landscape and thermal features

Scientific Reports ◽

10.1038/s41598-020-78755-8 ◽

2020 ◽

Vol 10 (1) ◽

Author(s):

Camila Lorenz ◽

Marcia C. Castro ◽

Patricia M. P. Trindade ◽

Maurício L. Nogueira ◽

Mariana de Oliveira Lage ◽

...

Keyword(s):

Remote Sensing ◽

Aedes Aegypti ◽

Vector Control ◽

Land Surface ◽

Urban Areas ◽

Negative Binomial ◽

Control Strategies ◽

Winter Season ◽

Landsat 8 ◽

Thermal Features

AbstractIdentifying Aedes aegypti breeding hotspots in urban areas is crucial for the design of effective vector control strategies. Remote sensing techniques offer valuable tools for mapping habitat suitability. In this study, we evaluated the association between urban landscape, thermal features, and mosquito infestations. Entomological surveys were conducted between 2016 and 2019 in Vila Toninho, a neighborhood of São José do Rio Preto, São Paulo, Brazil, in which the numbers of adult female Ae. aegypti were recorded monthly and grouped by season for three years. We used data from 2016 to 2018 to build the model and data from summer of 2019 to validate it. WorldView-3 satellite images were used to extract land cover classes, and land surface temperature data were obtained using the Landsat-8 Thermal Infrared Sensor (TIRS). A multilevel negative binomial model was fitted to the data, which showed that the winter season has the greatest influence on decreases in mosquito abundance. Green areas and pavements were negatively associated, and a higher cover of asbestos roofs and exposed soil was positively associated with the presence of adult females. These features are related to socio-economic factors but also provide favorable breeding conditions for mosquitos. The application of remote sensing technologies has significant potential for optimizing vector control strategies, future mosquito suppression, and outbreak prediction.

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Dynamic Daylight Metrics for Electricity Savings in Offices: Window Size and Climate Smart Lighting Management

Energies ◽

10.3390/en11113143 ◽

2018 ◽

Vol 11 (11) ◽

pp. 3143 ◽

Cited By ~ 6

Author(s):

Ignacio Acosta ◽

Miguel Ángel Campano ◽

Samuel Domínguez-Amarillo ◽

Carmen Muñoz

Keyword(s):

Energy Efficiency ◽

Net Present Value ◽

Energy Savings ◽

Performance Metrics ◽

Control Strategies ◽

Window Size ◽

Weather Conditions ◽

Worst Case ◽

Daylight Performance ◽

Dynamic Metrics

Daylight performance metrics provide a promising approach for the design and optimization of lighting strategies in buildings and their management. Smart controls for electric lighting can reduce power consumption and promote visual comfort using different control strategies, based on affordable technologies and low building impact. The aim of this research is to assess the energy efficiency of these smart controls by means of dynamic daylight performance metrics, to determine suitable solutions based on the geometry of the architecture and the weather conditions. The analysis considers different room dimensions, with variable window size and two mean surface reflectance values. DaySim 3.1 lighting software provides the simulations for the study, determining the necessary quantification of dynamic metrics to evaluate the usefulness of the proposed smart controls and their impact on energy efficiency. The validation of dynamic metrics is carried out by monitoring a mesh of illuminance-meters in test cells throughout one year. The results showed that, for most rooms more than 3.00 m deep, smart controls achieve worthwhile energy savings and a low payback period, regardless of weather conditions and for worst-case situations. It is also concluded that dimming systems provide a higher net present value and allow the use of smaller window size than other control solutions.

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Opportunities for winter prescribed burning in mixed conifer plantations of the Sierra Nevada

Fire Ecology ◽

10.1186/s42408-021-00120-5 ◽

2021 ◽

Vol 17 (1) ◽

Author(s):

Robert A. York ◽

Jacob Levine ◽

Kane Russell ◽

Joseph Restaino

Keyword(s):

Sierra Nevada ◽

Pinus Ponderosa ◽

Prescribed Burning ◽

Winter Season ◽

Basal Area ◽

Weather Conditions ◽

Shrub Cover ◽

Mixed Conifer ◽

Planted Forests ◽

Conifer Plantations

Abstract Background Young, planted forests are particularly vulnerable to wildfire. High severity effects in planted forests translate to the loss of previous reforestation investments and the loss of future ecosystem service gains. We conducted prescribed burns in three ~35-year-old mixed conifer plantations that had previously been masticated and thinned during February in order to demonstrate the effectiveness of winter burning, which is not common in the Sierra Nevada, California. Results On average, 59% of fine fuels were consumed and the fires reduced shrub cover by 94%. The average percent of crown volume that was damaged was 25%, with no mortality observed in overstory trees 1 year following the fires. A plot level analysis of the factors of fire effects did not find strong predictors of fuel consumption. Shrub cover was reduced dramatically, regardless of the specific structure that existed in plots. We found a positive relationship between crown damage and the two variables of Pinus ponderosa relative basal area and shrub cover. But these were not particularly strong predictors. An analysis of the weather conditions that have occurred at this site over the past 20 years indicated that there have consistently been opportunities to conduct winter burns. On average, 12 days per winter were feasible for burning using our criteria. Windows of time are short, typically 1 or 2 days, and may occur at any time during the winter season. Conclusions This study demonstrates that winter burning can be an important piece of broader strategies to reduce wildfire severity in the Sierra Nevada. Preparing forest structures so that they can be more feasible to burn and also preparing burn programs so that they can be nimble enough to burn opportunistically during short windows are key strategies. Both small landowners and large agencies may be able to explore winter burning opportunities to reduce wildfire severity.

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GEOMATIC TECHNIQUES FOR THE OPTIMIZATION OF SKI RESOURCES

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xliii-b2-2020-1009-2020 ◽

2020 ◽

Vol XLIII-B2-2020 ◽

pp. 1009-1016

Author(s):

I. Aicardi ◽

S. Angeli ◽

N. Grasso ◽

A. M. Lingua ◽

P. Maschio

Keyword(s):

Heat Waves ◽

Low Cost ◽

Winter Season ◽

Heavy Rain ◽

Weather Conditions ◽

Fundamental Aspect ◽

Carbon Neutrality ◽

Aerial Vehicle ◽

Gnss Receivers ◽

Processing Techniques

Abstract. Climate change is already affecting the entire world, with extreme weather conditions such as drought, heat waves, heavy rain, floods and landslides becoming more frequent, including Europe. In according to Paris agreement and relative European announcement of Carbon neutrality (by 2050), the saving of water and energy supplies is a fundamental aspect in the management of resources in production, sports, hospitality facilities and so on. Some methodologies for the optimization of the consumption of natural resources are required. This article describes an activity aimed at measuring, monitoring and analysing the thickness of the snowpack on the ski slopes during the winter season to permit a sustainable approach of snowmaking in alpine ski areas . The authors propose a methodology based on the integration of multitemporal surface (ground/snow) survey by Autonomous Aerial Vehicle (AAV) and low cost GNSS receivers mounted on snow groomers for a RTK (Real Time Kinematic) solution. To obtain a complete snow surface digital models with poor detailed images on ski slopes, some pre-processing techniques have been analysed to locally improve contrast and details with a local high pass filtering. The methodology has been employed in two study areas (Limone Piemonte, Prato Nevoso) located in the province of Cuneo, in the southern alpine area of Piedmont.

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