Luis Miranda

Luis Miranda
Hochschule für nachhaltige Entwicklung Eberswalde · Faculty of Forest and Environment

Dr. rer. agr.

About

31
Publications
5,550
Reads
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61
Citations
Citations since 2016
19 Research Items
59 Citations
201620172018201920202021202202468101214
201620172018201920202021202202468101214
201620172018201920202021202202468101214
201620172018201920202021202202468101214

Publications

Publications (31)
Article
Full-text available
The increasing number of severe storm events is threatening European forests. Besides the primary damages directly caused by storms, there are secondary damages such as bark beetle outbreaks and tertiary damages due to negative effects on the market. These subsequent damages can be minimized if a detailed overview of the affected area and the amoun...
Article
Full-text available
This work presents an adaptive architecture that performs online learning and faces catastrophic forgetting issues by means of an episodic memory system and of prediction-error driven memory consolidation. In line with evidence from brain sciences, memories are retained depending on their congruence with the prior knowledge stored in the system. In...
Preprint
Full-text available
This work presents an adaptive architecture that performs online learning and faces catastrophic forgetting issues by means of episodic memories and prediction-error driven memory consolidation. In line with evidences from the cognitive science and neuroscience, memories are retained depending on their congruency with the prior knowledge stored in...
Preprint
Full-text available
This work deals with the portability of greenhouse models, as we believe that this is a challenge to their practical usage in control strategies under production conditions. We address this task by means of adaptive neural networks, which re-adjust their weights when transferred to new conditions. Such an adaptive account for computational models i...
Article
Artificial neural networks (ANN) have been successfully used for modelling climate and plant signals in greenhouses. This work shows the combination of two ANN models, built and trained separately, and then coupled together. Both models were trained using a data set consisting of 3 complete cultivation periods (2011 until 2013) of 2 venlo-type gree...
Article
Artificial neural networks (ANN) have been successfully used for modelling climate and plant signals in greenhouses. This work shows the combination of two ANN models, built and trained separately, and then coupled together. Both models were trained using a data set consisting of 3 complete cultivation periods (2011 until 2013) of 2 venlo-type gree...
Poster
Full-text available
Varying conditions of cultivation of vegetables inside a city’s boundaries were analyzed. As such, in the city of Berlin flower boxes with lettuce were installed at 20 spots at different heights, with different light conditions and air pollution levels. In order to exclude heavy metal uptake due to soil contamination, lettuce was seeded in a standa...
Article
The trend in greenhouse automation systems is to listen to plant signals while trying to minimize the ecological footprint of the vegetable production. In this work,we present a model based on artificial neural networks (ANN), which can be used to support the measuring of plant signals and help to improve the reliability of the data collected. This...
Poster
Full-text available
Due to the fact that greenhouses need big amounts of energy (esp. for heating purposes in temperate zones), there is a growing interest on new technical systems to save energy and fossil resources. In this context, the inclusion of active solar thermal systems in greenhouses for energy harvesting and plant production represents an interesting solut...
Article
Full-text available
Growing environmental and sustainability concerns have driven continual modernization of horticultural practices, especially for urban farming. Controlled environment and soilless production methods are increasing in popularity because of their efficient resource use and intensive cropping capabilities. However, some popular substrates used for hyd...
Article
This study evaluated if an Artificial Intelligence climate forecasting model can be considered as a useful tool for saving energy in semi-closed greenhouses. Preliminary results are presented on the 5-Minutes prediction of the internal air temperature and humidity modeled with Artificial Neural Networks (ANN). Since the final goal of the simulation...
Article
This study evaluated if an Artificial Intelligence climate forecasting model can be considered as a useful tool for saving energy in semi-closed greenhouses. Preliminary results are presented on the 5-Minutes prediction of the internal air temperature and humidity modeled with Artificial Neural Networks (ANN). Since the final goal of the simulation...
Article
The water and carbon footprint of the presented dried tomato value chain is compared to the conventional process. The coupling of pre- and post-harvest processes, namely growing and drying respectively, is analyzed for resource consumption optimization. The growing system of tomatoes (Solanum lycopersicon L. cv, Pannovy) in an energy efficient gree...
Presentation
Full-text available
Seit vielen Jahren wird das Konzept semigeschlossenes Gewächshaus für den Gemüsebau weiterentwickelt. Ziel ist es, den Heizenergiebedarf zu senken, die durch Verwendung fossiler Brennstoffe entstehenden CO2-Emissionen drastisch zu vermindern und höhere Erträge durch länger zu realisierende CO2-Anreicherung am Tage zu erzielen. Für die Wirtschaftlic...
Presentation
Full-text available
We present an application of time-series prediction to the climate inside a greenhouse. We use an Artificial Neural Network (ANN) model to predict the air temperature and relative humidity inside the greenhouse 5 minutes in advance. Such a model generates a One Step Prediction (OSP). We iterate this OSP to get Long-Term Predictions (LTP), thus gene...
Article
Das Prinzip geschlossener Gewächshäuser zur Nutzung solarer Überschusswärme stellt einen ressourcenschonenden Ansatz hinsichtlich des Energieaufwands im geschützten Gemü-seanbau dar. Dabei wird überschüssige Wärmeenergie mittels Kühlsystem dem Gewächs-haus entzogen und in einen Wärmespeicher geleitet (Abb. 1). Diese kann wiederum für Heizzwecke ein...
Article
Full-text available
2009 to 2014, the joint research program ZINEG is carried out in Germany. Its main aim is to reduce the consumption of fossil fuels and hence the CO2 emissions up to 90% for production in greenhouses. At Humboldt University the research is focused on using a greenhouse system as solar thermal collector with above-ground heat storage. During the tom...
Article
The main objective of this paper is to fit temperature and humidity as a natural system of ordinary differential equations for different scenarios with asymmetrical histograms, as well as satisfying somehow sensitivity and robustness by using their derivatives and integrals. First, the scenario with lower humidity corresponding to dry conditions is...
Article
Full-text available
The limited supply of fossil fuels has increased the potential for using closed greenhouses or solar collector greenhouses, where the heat accumulation gives an opportunity to recover and store thermal energy. This study focuses on a Venlo-type semiclosed greenhouse (307 m2) equipped with cooling, heating, and CO2 enrichment. Also, there are additi...
Poster
Full-text available
Werden Gewächshäuser geschlossen betrieben, kommt es zu einer Akkumulation von Wasserdampf im Inneren. Dies kann phytosanitäre Probleme verursachen oder physiologische Funktionen wie die Fruchtbildung beeinflussen. Hierzu gibt es differente Aussagen. So konnte bei Versuchen am IGZ ein Zusammenhang zwischen hoher relativer Luftfeuchte und vermindert...
Conference Paper
Full-text available
Um Gewächshäuser als Sonnenkollektoren zu nutzen, müssen Kühleinrichtungen im Inneren eingesetzt werden, um die überschüssige Wärme bei hohen solaren Einstrahlungsraten entziehen zu können. Aus physikalischer Sichtweise ist der Dachraum der geeignete Ort dafür, da warme und feuchte Luft infolge des Dichteunterschiedes aufsteigen. Deshalb wurden im...
Poster
Full-text available
The conventional method for calculating the heat demand in greenhouses uses the difference of inside and outside air temperature as well as heat consumption coefficients (Ucs-values). These values give an indication of the sensible heat loss at a temperature difference of 1 K on 1 m2 covering material. However, greenhouses also lose latent heat by...

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Projects

Projects (2)
Project
The goal of the project “Development of a sensor based intelligent greenhouse management system” is to establish a system for increasing the efficiency of production under glass. This system accesses on huge quantities of data recorded in different greenhouses and determines optimal culture control strategies. Furthermore, the resource consumption is calculated and can be processed further to important parameters (e.g., carbon footprint, resource input per yield unit etc.), which can be used for costumer communication or to compare the resource consumption of different enterprises. This system includes intelligent sensors, which show plant responses, such as photosynthesis, fruit growth and plant transpiration, under different cultivation conditions. In addition, different scenarios of cultivation strategies can be simulated on the computer. As such, the aim is to reduce raw material consumption caused by an optimal crop management. The work plan is split into three project phases. During the first project phase, the work is focused on the description of the system and on the definition of the system boundaries, as well as on the selection and optimization of the sensor technology. The largest part of the first project phase covers data acquisition, as well as the model development and the calculation of resource balances. Structuring and preparation of the greenhouse management system take place during the following project phase. The data from the first and the second project phase must be merged into one overall system and evaluation tools have to be implemented. As such, the communication with existing systems is an important prerequisite. The third and last project phase is focused on the validation and optimization of the greenhouse management system. This phase also serves the purpose to determine strategies in order to minimize the recourse input for the production under glass. Project duration: 1. September 2017 – 31. October 2020 Funding code: 2815701315 Project coordinator: Dr. Dennis Dannehl The project is supported by funds of the Federal Ministry of Food and Agriculture(BMEL) based on a decision of the Parliament of the Federal Republic of Germany via the Federal Office for Agriculture and Food (BLE) under the innovation support programme.
Archived project
Currently, about 30% of Germany’s annual electricity is generated from renewable sources like wind and the sun. Until 2030, the German government wants to increase this proportion to 50%. Other EU nations have similar plans. Predictive models indicate that this aim will lead to an increase in volatility of the energy supply and will affect electricity pricing. Thus, a temporary oversupply of renewable energy (e.g. at strong wind conditions) can lead to a low price of electricity at the energy exchange market. Otherwise, the frequency of annual hours with high pricing will be increased in case of insufficient solar radiation or wind speed. Therefore, flexible capacities are required for energy storage as well as consumption. Related to the power supply of commercial greenhouses, two approaches were considered at Humboldt University: - Irrigation water resources could be used as large energy storages for greenhouse air-conditioning in order to obtain the benefits from fluctuations of electricity pricing - The energy for greenhouse air-conditioning will be increasingly used when electricity is cheap The feasibility study named ELGEVOS started in 2015 and is supported by funds of the German Government’s Special Purpose Fund held at Rentenbank. Website: www.elgevos.de