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Solar energy is one of the most abundant and available forms of renewable energy. Reliance on the electricity network can be decreased and net-zero energy achieved by mounting photovoltaic power on the tops of houses. Photovoltaic arrays can also change how the roof’s surface reacts to its environment. The influence of the structural system of a roof and weather on the energy consumption of a building is important. This research is concerned with focusing on the indirect effect of solar photovoltaic rooftop panels (shading effect) on the roof surface to see whether this effect is worth studying and calculating the total electrical load in the residential sector. Photovoltaic panels were modeled as a shading device, and the Integrated Environmental Solution-Virtual Environment Software was used to anticipate the monthly decline and growth in heating and cooling loads associated with the roof level. The influence of a photovoltaic system on a building’s roof-related energy load was measured concerning low-rise residential buildings in Mafraq city, which belongs to a mild dry-warm temperature zone. The findings indicated that a solar roof structure decreased heat loss by 4.85% in the summer and boosted heat transfer by 5.54% in the winter. The results highlight that renewable energy is very important in our times due to climate change and the increased demand for electricity by the residential sector, which is stimulated to find multiple ways to decrease and adapt to this change, and the aim of this paper helps to encourage to use solar energy by identifying the indirect effect of solar panels on building’s rooftops. This investigation also focuses on the value of offering essential instructions to who is concerned to the utilization of alternative energy to heat and cool structures, also will educate the public on a building’s total energy requirements, which is critical for future green structure design., This work was funded by the Spanish Ministry of Science, Innovation, and Universities under the program “Proyectos de I + D de Generacion de Conocimiento” of the national program for the generation of scientific and technological knowledge and strengthening of the R + D+I system with grant number PGC2018-098813-B-C33 and from UAL-FEDER 2020, Ref. UAL2020-TIC-A2080.

Due to its excessive energy consumption, the building sector contributes significantly to greenhouse gas (GHG) emissions. The type of thermal comfort models used to maintain the comfort of occupants has a direct influence on forecasting heating and cooling demands and plays a critical role in reducing actual energy usage in the buildings. In this research, a typical residential building was simulated to compare the heating and cooling loads in four different Jordanian climates when using an adaptive thermal model versus the constant setting of temperature limits for air-conditioning systems (19–24 ◦C). The air-conditioning system with constant temperature settings worked to sustain thermal comfort inside the building, resulting in a significantly increased cooling and heating load. By contrast, significant energy savings were achieved using the temperature limits of an adaptive thermal model. These energy savings equated to 1533, 6276, 3951, and 3353 kWh, which represented 29.3%, 80.5%, 48.5%, and 67.5% of the total energy used for heating and cooling for zones one, two, three, and four, respectively, This work was funded by the Spanish Ministry of Science, Innovation and Universities under the program “Proyectos de I + D de Generacion de Conocimiento” of the national program for the generation of scientific and technological knowledge and strengthening of the R + D + I system with grant number PGC 2018-098813-B-C 33 and from UAL-FEDER 2020, Ref. UAL2020-TIC-A2080. The authors would like to acknowledge: German Jordanian University, AnNajah National University, University of Almeria and the Huelva University for facilitating this research

The agri-food sector is in constantly renewing, continuously demanding new systems that facilitate farmers&acute, work. Efficient agricultural practices are essential to increasing farm profitability, and reducing water consumption can be achieved by real-time monitoring of water needs. However, the prices of automatic systems for collecting data from several sources (soil and climate) are expensive and their autonomy is very low. This paper presents a low-consumption solution using the Internet of Things (IoT) based on wireless sensor networks (WSNs) and long-range wide-area network (LoRaWAN) technologies. By means of low-power wide-area network (LPWAN) communication, a farmer can monitor the state of crops in real time thanks to a large number of sensors connected wirelessly and distributed across the farm. The wireless sensor node developed, called BoXmote, exhibits very low power, since it has been optimized both in terms of hardware and software. The result is a higher degree of autonomy than commercial motes. This will allow the farmer to have access to all of the information necessary to achieve an efficient irrigation management of his crops with full autonomy.

The effective and regular remote monitoring of agricultural activity is not always possible in developing countries because the access to cloud-based geospatial analysis platforms or expensive high-resolution satellite images are not always available. Herein, using paid high-resolution satellite images first and then free medium-resolution satellite images, we aimed to develop a cost-free, affordable method for regularly mapping the spatial distribution of sicklebush (Dichrostachys cinerea), an archetypal allochthonous invasive plant in Cuba that is becoming impossible to control owing to its rapid growth in areas planted with sugar cane in the Trinidad-Valle de los Ingenios area (Cuba), a UNESCO World Heritage Site. Two types of images were used (WorldView-2 and Landsat-8); these were subjected to supervised classification, with accuracy values of 88.7% and 93.7%, respectively. Vegetation cover was first derived from the purchased WorldView-2 image, and this information was then used as the training field to obtain spectral signatures from the Landsat-8 free image so that Landsat images may be regularly used to monitor D. cinerea infestations. The results obtained in the spatial distribution map for sicklebush in the Landsat-8 images had an overall reliability of 93.7% and a Kappa coefficient reliability of 91.9%. These values indicate high confidence in the results, which suggests that sicklebush has invaded 52.7% of the total 46,807.26-ha area of the Trinidad-Valle de los Ingenios. This process proved extremely effective and demonstrated the benefits of using high-resolution spatial images from which information can be transferred to free satellite images with a larger pixel size., This study was funded by the Spanish Agency for International Development and Cooperation (AECID) [grant number 11 CAP2 1423] and the Andalusian Agency for International Development and Cooperation (AACID) [grant number 2017DEC013]