Due to the fact that irrigation networks are water and energy hungry and that both resources are scarce, many strategies have been developed to reduce this consumption. Solar energy sources have emerged as a green alternative with lower energy costs and, consequently, lower environmental impacts. In this work, a new methodology is proposed to select a scheduled program for irrigation which minimizes the number of photovoltaic solar panels to be installed and which better fits energy consumption (calculated for discrete potential combinations, assisted by programming software) to available energy obtained by panels without any power conditioning unit. Thus, the irrigation hours available to satisfy the water demands are limited by sunlight, the schedule type of irrigation has to be rigid (rotation predetermined), and the pressure at any node has to be above the minimum pressure required by standards. A case study was undertaken and, after running the software 10 5 times, the best result was an irrigation schedule which satisfied all the requirements, involving the installation of 651 solar panels and energy consumption of 428.74 kWh per day, to deliver water to orchards of different varieties of citrus fruit spread over 167.7 ha.
Designing solar strategies is a powerful step forward to set up an adequate residential house in terms of energy. Many types of research have simulated the energy needs for residential buildings. Designing an improper installation can contribute to a growth in the overall energy expenditure in ensuring thermal comfort. The use of solar thermal processes in Slovakia is on a rise as compared to recent years. This study models twelve solar water heating systems created on the roof of the household. Solar energy techniques are carried out to comply with the demands of heating and domestic hot water. The analysis deals with the most efficient alternative for the arranged solar systems of the building. Considering these installations and the corresponding overall prices of machinery, the best workable alternative is selected. The potential energy performance of auxiliary heating and the energy output of the solar thermal installation are examined. The required amounts of the different energy contributions are modelled and simulated in specific software for a family house in Kosice, Slovakia. We determine the limits of the design for an apartment and analyse which procedure is used to provide the typical average water expenditure and heating need, covering a multi-criteria analysis considering costs, energy, and life cycle analysis of every installation. This approach can support professionals to decide the best scheme considering these criteria, and this method can be satisfactorily applied. In these conditions, converting a conventional gas boiler into a solar thermal system involves monthly economic savings of around EUR 140–250, with payback periods of 2.5–7 years. The energy requirements are fully covered by the solar thermal schemes and the life cycle assessment resulted in reasonable impacts on the environment.
Water quality and scarcity are key topics considered by the Sustainable Development Goals (SDGs), institutions, policymakers and stakeholders to guarantee human safety, but also vital to protect natural ecosystems. However, conventional approaches to deciding the suitability of water for drinking purposes are often costly because multiple characteristics are required, notably in low-income countries. As a result, building right and trustworthy models is mandatory to correctly manage available groundwater resources. In this research, we propose to check multiple classification techniques such as Decision Trees (DT), K-Nearest Neighbors (KNN), Discriminants Analysis (DA), Support Vector Machine (SVM), and Ensemble Trees (ET) to design the best strategy allowing the forecast a Water Quality Index (WQI). To achieve this goal, an extended dataset characterized by water samples collected in a total of twelve municipalities of the Wilaya of Naâma in Algeria was considered. Among them, 151 samples were examined as training samples, and 18 were used to test and confirm the prediction model. Later, data samples were classified based on the WQI into four states: excellent water quality, good water quality, poor water quality, and very poor or unsafe water. The main results revealed that the SVM classifier obtained the highest forecast accuracy, with 95.4% of prediction accuracy when the data are standardized and 88.9% for the accuracy of the test samples. The results confirmed that the use of machine learning models are powerful tools for forecasting drinking water as larger scales to promote the design of efficient and sustainable water quality control and support decision-plans.
Converting a water pressurised distribution network into an off-grid pumping station supplied by solar photovoltaics represents a challenge for utility managers, user demand assessments evaluate the energy generated in a solar-powered systems to establish energy consumption. This work includes quantifying potential investments and economic savings that could be achieved, as well as the payback period which results as an indicator of the suitability of adapting to a power supply utilising solar panels. A tool (UAsolar) to aid practitioners has been developed, it requires a calibrated hydraulic model to account for the energy requirements in the water delivery process of pressurised networks. The authors encourage students, professionals, and decision-makers to use this tool to identify potential efficiency gains (e.g., delivery schedule, reduction of water use) and to synchronise energy production and consumption. Users can get results with low computational time using the software on six pressurised distribution networks. Practitioners should note that the irrigation networks have sized installations with a few photovoltaic modules, while in urban pressurised networks the results show larger installations are required. In addition, irrigation network managers can match energy demand with energy production by changing consumption over time, this could reduce the quantity of modules required and remove the need for energy storage. The payback period ranges from 6.08 to 13 years for the cases where the investment is recovered—(values that show that this investment yields a high return as the lifetime of the PV modules is 25 years). However, one municipality among those studied shows that in some scenarios it is not viable to convert networks into a standalone system. Graphical abstract
Solar energy is one of the most promising green energy sources. On-grid photovoltaic installations supply energy to consumers as a support energy source, but in isolated areas, it comes as the unique source. The decision-maker must dimension the installation, maintaining system performance with reasonable investments. In some scenarios, the utility manager can handle the energy delivered to consumers as every subsystem can be independently connected. A strategy for scheduling the energy consumption to decrease the number of photovoltaic modules required in a standalone system is proposed here. The problem formulation corresponds to generalising a more specific problem before published. We presented a real case study being the groups of hydrants that provide water to crops in a pressurized irrigation system for energy consumption to schedule.
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