Hourly Time Step Research Articles

Stochastic Hybrid Event Based and Continuous Approach to Derive Flood Frequency Curve

This study proposes a methodology that combines the advantages of the event-based and continuous models, for the derivation of the maximum flow and maximum hydrograph volume frequency curves, by combining a stochastic continuous weather generator (the advanced weather generator, abbreviated as AWE-GEN) with a fully distributed physically based hydrological model (the TIN-based real-time integrated basin simulator, abbreviated as tRIBS) that runs both event-based and continuous simulation. The methodology is applied to Peacheater Creek, a 64 km2 basin located in Oklahoma, United States. First, a continuous set of 5000 years’ hourly weather forcing series is generated using the stochastic weather generator AWE-GEN. Second, a hydrological continuous simulation of 50 years of the climate series is generated with the hydrological model tRIBS. Simultaneously, the separation of storm events is performed by applying the exponential method to the 5000- and 50-years climate series. From the continuous simulation of 50 years, the mean soil moisture in the top 10 cm (MSM10) of the soil layer of the basin at an hourly time step is extracted. Afterwards, from the times series of hourly MSM10, the values associated to all the storm events within the 50 years of hourly weather series are extracted. Therefore, each storm event has an initial soil moisture value associated (MSM10Event). Thus, the probability distribution of MSM10Event for each month of the year is obtained. Third, the five major events of each of the 5000 years in terms of total depth are simulated in an event-based framework in tRIBS, assigning an initial moisture state value for the basin using a Monte Carlo framework. Finally, the maximum annual hydrographs are obtained in terms of maximum peak-flow and volume, and the associated frequency curves are derived. To validate the method, the results obtained by the hybrid method are compared to those obtained by deriving the flood frequency curves from the continuous simulation of 5000 years, analyzing the maximum annual peak-flow and maximum annual volume, and the dependence between the peak-flow and volume. Independence between rainfall events and prior hydrological soil moisture conditions has been proved. The proposed hybrid method can reproduce the univariate flood frequency curves with a good agreement to those obtained by the continuous simulation. The maximum annual peak-flow frequency curve is obtained with a Nash–Sutcliffe coefficient of 0.98, whereas the maximum annual volume frequency curve is obtained with a Nash–Sutcliffe value of 0.97. The proposed hybrid method permits to generate hydrological forcing by using a fully distributed physically based model but reducing the computation times on the order from months to hours.

Techno-economic analysis of combined heat pump and solar PV system for multi-family houses: An Austrian case study

With the increasing amount of building renovations in Austria, the potential increases for replacing conventional national gas heating systems with heat pumps (HP) and thereby reduce CO2 emissions particularly when combined with solar photovoltaics (PV). The Austrian subsidization scheme for HP and PV systems are different for every state, creating confusion and inconstancy for potential adopters. This study provides a parametric techno-economic analysis of PV + HP systems to identify the critical economic parameters on profitability and make policy recommendations. A case study in Vienna is modelled using demand from the Building Model Generator and black box efficiency models for the HP and PV simulated with hourly time steps. The results show that both air-source and ground source heat pumps are currently profitable with PV under current subsidy schemes. The benefit-to-cost ratio (BCR) is highly influenced by capital costs and subsidies, however natural gas prices have the greatest influence. Increasing natural gas prices by 0.01 €/kWh, or 17%, is enough to replace all other complicated subsidies for both HP and PV. This is equivalent to a carbon emissions price of 33 €/ton and could result in a reduction of CO2 emissions in multi-family houses by approximately 45%–60%.

Grid integration of renewable energy in Qatar: Potentials and limitations

This study presents an analysis of the current electricity supply grid in Qatar and investigates the potential of integrating various renewable energy sources (RES) into the grid. The hourly demand profile for electricity, cooling, and freshwater is used to investigate the impact of wind turbines, photovoltaics, and concentrated solar power integration on the environmental emissions and total annual cost of the systems. A one-year dynamic analysis based on an hourly time step is conducted using the EnergyPLAN tool. The results show that large-scale installations of renewable energy technologies can decrease the emission rate from electricity production at a relatively lower cost. A reference case scenario representing the current state of affairs along with six other cases representing various single and hybrid renewable energy combinations for integration into the electricity supply is presented in terms of their ability to avoid excess electricity production. The eighth case scenario presents a hypothetical electricity demand for the year 2025. The results show that increasing the share of RES in electricity production is possible by as much as 80%. The optimum cases for the deployment of wind, photovoltaic (PV), and concentrated solar power (CSP) with storage technologies presented a 28.3%, 23.4%, and 38.2% share to electricity produced, respectively. The market economic simulation shows that the total annual cost for some of the scenarios that integrated renewable energy was lower than that of the reference case currently deployed in the country.

Long-term and event-scale sub-daily streamflow and sediment simulation in a small forested catchment

ABSTRACT In small catchments, the time interval most commonly used for simulation – daily or monthly – may not be sufficient to accurately capture the time distribution of hydrological processes. In this paper, the Soil and Water Assessment Tool (SWAT) was used to perform an hourly long-term streamflow and sediment load simulation in the small (4.8 km2) and forested Aixola catchment (northern Spain). From this simulation, 10 runoff events were tested; the most satisfactory results for streamflow were obtained under wet antecedent conditions. However, simulated sediment load was underestimated during the peaks and remained high towards the end of the event. Furthermore, the influence of the precipitation time step (1–4 h, daily) was not relevant in the streamflow simulation but does influence the sediment simulation. The best results were achieved with the daily step simulations obtained at an hourly time step. This paper shows that sub-daily modelling improves water and especially sediment yield results; however, improvements are still needed in timing-related routines.

Electrification and renewable energy nexus in developing countries; an overarching analysis of hydrogen production and electric vehicles integrality in renewable energy penetration

As the world continues to fight against climate change, renewables have emerged as an imminent solution, however, a model that can help (developing) countries to effectively migrate to a renewable energy-based power sector is limited in literature. In this study, a comprehensive electricity generation analysis based on an hourly time-step is done in a bid to solve to electrification crisis in developing countries. This study aims to present the feasible pathways with which sustainable electrification can be achieved. This is done by considering the financial feasibility, environmental sustainability, and technical viability of different technological combinations. In comparison to existing literature, this study is novel as the integration of both renewable energy sources and a fossil fuel source is analyzed. With Nigeria being the study area, the integration of five renewable energy-based technologies namely; offshore wind power plant, onshore wind power plant, solar photovoltaic system, concentrated solar power plant, and hydropower plant as well as pumped hydro storage system is considered within the scope of this study. Based on the results of the analysis, out of the ninety-nine different combinatory scenarios modeled, the most viable solution with the use of renewables and natural gas is the combination of natural gas power plants, on-shore wind power plants, and solar photovoltaic systems. This requires a large capacity installation (32,500 MW of natural gas power plant, 12,000 MW of solar photovoltaic plant, and 7000 MW of on-shore wind power plant) as well as a total annual cost and an investment cost of $ 22B and $ 48.7B. Furthermore, solving the poised problem based on renewable energy technologies will only require the combination of on-shore wind power plants, hydropower plants, solar photovoltaic system, and pumped hydro storage system. The integration of plug-in battery electric vehicles and hydrogen production will help maximize the electricity produced from the renewable energy-based power systems. This will also reduce the overall carbon emission from the country at large. Beyond providing feasible scenarios and plans to tackle the existing electricity production, guidelines on steps (not to take) to tackle the electrification problem are embedded in this study.

Feasibility analysis of decentralized hybrid rainwater-graywater systems in a public building in Japan

The development of hybrid rainwater-graywater systems (HRG) has greatly alleviated urban water scarcity. However, the HRGs in public buildings have rarely been explored, which had limited the popularity of hybrid systems. In this study, a campus in Japan was selected to evaluate the feasibility of HRGs in public buildings. A water balance model with an hourly time step was performed to quantify the performance of the rainwater and graywater subsystems in the HRGs. Second, the electricity consumption of the HRGs was evaluated. Then, a detailed life cycle cost model was designed to calculate the economic benefit of the HRGs under the current and optimization scenarios. Finally, the results obtained are compared with HRGs in residential and commercial buildings to discuss the advantages of HRGs in public buildings. The results indicate that the promotion of HRGs in public buildings can not only achieve higher water-saving efficiency than other building types but also reduce electricity consumption in comparison with the traditional water supply methods. The economical unfeasibility of HRGs is caused by the waste of excess reclaimed water and high maintenance costs. HRGs in public buildings has the potential to be promoted preferentially in regions where the water tariff is higher than 880 JPY/m3 or the non-potable water tariff is set to at least 200 JPY/m3.

Demonstration of the integrated rural energy planning framework for sustainable energy development in low-income countries: Case studies of rural communities in Nigeria

With the vast rural population, the availability of locational renewable energy resources and limited finances in low-income (developing) countries, there is the need for an integrated and holistic energy planning framework to unlock their potential for achieving energy-based economic development. The integrated rural energy planning framework provides a unified road map for energy planning and system design with renewable energy integration geared towards improving energy access in rural areas. Therefore, this study presents the application of case studies to this framework to foster improved localized renewable energy access in low-income (developing) countries. Using rural Nigeria as the geographic scope, the site identification and selection and the locational estimation of annual energy demand steps of the framework are performed for rural areas. Utilizing GIS-based tools, rural communities are identified, and their viable renewable energy resources are estimated. Adopting the reference building approach, seasonal disaggregated energy demand profiles are obtained at community scale with an hourly time-step. These are obtained in the absence of smart-metering equipment and measured datasets on energy use. The obtained results provide insights to inform localized energy policy design by combining rural electrification with energy conservation strategies. This framework has wide applicability across areas with similar energy access and rural development issues especially in sub-Saharan Africa and developing Asia.

Network representation in hydrological modelling on urban catchments in data-scarce contexts: A case study on the Oued Fez catchment (Morocco)

Study regionOued Fez, Morocco. Study focusUrban catchments are heterogeneous in terms of land use and have both natural and artificial drainage networks. Modelling them is not a straightforward task especially in data-scarce settings. This study investigates network representation in hydrological modelling using field data collected between 2008 and 2018 on the Oued Fez catchment. The road network is used as a proxy for the stormwater network. Two production functions, SCS (1972) and a linear reservoir, are coupled to the lag and route transfer function. Three types of land use classes are used. Tests are carried out at hourly and 5-minute time steps using both the natural and modified drainage networks. New hydrological insights for the regionFifty-three rainfall–runoff events are monitored on the urban part of the catchment over the 2008–2018 period. The highest rainfall values are recorded in 2008/2009, while the highest peak flow values are recorded in 2017/2018. This is due to stream channelisation and increased stormwater network coverage. Using the road network with minimal land use classes improves the model performance at both the hourly and 5-minute time steps.

Modeling the response of soil moisture to climate variability in the Mediterranean region

Abstract. Future climate scenarios for the Mediterranean region indicate a possible decrease in annual precipitation associated with an intensification of extreme rainfall events in the coming years. A major challenge in this region is to evaluate the impacts of changing precipitation patterns on extreme hydrological events such as droughts and floods. For this, it is important to understand the impact of climate change on soil moisture since it is a proxy for agricultural droughts, and the antecedent soil moisture condition plays a key role on runoff generation. This study focuses on 10 sites, located in southern France, with available soil moisture, temperature, and precipitation observations for a 10-year time period. Soil moisture is simulated at each site at the hourly time step using a model of soil water content. The sensitivity of the simulated soil moisture to different changes in precipitation and temperature is evaluated by simulating the soil moisture response to temperature and precipitation scenarios generated using a delta change method for temperature and a stochastic model (the Neyman–Scott rectangular pulse model) for precipitation. Results show that soil moisture is more impacted by changes in precipitation intermittence than precipitation intensity and temperature. Overall, increased temperature and precipitation intensity associated with more intermittent precipitation leads to decreased soil moisture and an increase in the annual number of days with dry soil moisture conditions. In particular, a temperature increase of +4 ∘C combined with a decrease of annual rainfall between 10 % and 20 %, corresponding to the current available climate scenarios for the Mediterranean, lead to a lengthening of the drought period from June to October with an average of +28 d of soil moisture drought per year.

Modelling of an Existing Neutral Temperature District Heating Network: Detailed and Approximate Approaches

This paper deals with the modelling of an existing neutral-temperature district heating network, meaning that the distribution temperature is close to the ambient temperature, with decentralised heat pumps. The considered case is located in Ospitaletto, Italy. Heat sources are given by industrial waste heat at about 25 °C and aquifer wells at about 15 °C. Two models are used to analyse the network: a detailed model able to calculate local values of operating parameters and an approximate model focused on energy balances aggregating all users with a lumped demand. Both models include the behaviour of heat pumps, a feature not available in other district heating modelling tools. An entire year of operation is considered, with an hourly time resolution. Load profiles are provided as inputs, while the main outputs consist of energy balances and primary energy consumptions. The corresponding results are compared, showing a reasonable agreement, where the approximate model underestimates the overall electricity consumptions by about 15% with respect to the detailed model. On the other hand, the different information levels and execution times (the detailed model requires about 30 min to solve the considered network for a full year with hourly time steps, while the approximate model is almost immediate) make the two models suitable for different purposes, like the simulation of control solutions for the detailed one and scenario analysis for the other.

Techno-economic impact of collective self-consumption on different energy-related investments for a condominium

Energy Communities (ECs) are gaining a key role in supporting the transition towards a low carbon economy in EU, since they combine the promotion of efficient energy practices with an expected positive social impact. Legislators are resorting to the concept of collective self-consumption as a tool to favor the electrification of energy needs and the diffusion of renewable energy sources. This work presents the first results in this field of the ENERGYNIUS project, co-funded by Regione Emilia-Romagna, focusing on the economic feasibility of investment decisions that would increase energy efficiency and renewables’ penetration in the household sector. To this purpose, a mathematical model has been developed, featuring an hourly time step definition of the internal and external energy exchanges and an accurate modelling of the electricity billing. It has been applied to an Italian condominium case study where the energy requirements of the inhabitants – in terms of electricity, heating, cooling and private transportation – have been considered. Thanks to the model, it is possible to compare increasing levels of electrification (e.g., photovoltaic panels, heat pumps, electric vehicles and batteries) and assess the benefits of collective selfconsumption on the condominium cash flows and environment (fossil CO2 emissions).

Model-based energy analysis of a dry room HVAC system in battery cell production

The operation of drying rooms is an essential part of battery cell production, in order to provide a save and well conditioned environment during the cell assembly. A specially dimensioned Heating, Ventilation and Air Conditioning (HVAC) system is required to operate a dry room, which depends on a large number of parameters and accounts for a substantial part of the total energy demand in battery cell production. Therefore, a dry room significantly contributes to the energy embodied in battery cells and affects their cost and environmental footprint. In this context, model-based, quantitative analysis are of interest in order to dynamically evaluate the effects of changed of ambient conditions at different locations. In this paper, we investigate the operation of an existing drying room through a case study at the Battery LabFactory Braunschweig with a physical simulation model. We validate the model against recorded measurement data in high temporal resolution. The model is able to represent the measured data of the total energy demand over one month at an hourly time step with only 3.27 % deviation. Using the validated simulation model of the HVAC system, we examine the operation of the system at different locations regarding their economic and ecological footprint. To achieve this, we virtually relocate the system to five different locations around the world and operate it over a typical year at each location. We carry out an economic and environmental assessment for each site under consideration and for each location we report relevant KPIs that are independent from production throughput and potentially transferable to other use cases. Such investigations allow interesting findings to be derived for practical applications in brown-field applications, but also for the planning of new systems at different locations.

Exploring diurnal cycles of surface urban heat island intensity in Boston with land surface temperature data derived from GOES-R geostationary satellites

The surface urban heat island (SUHI) is one of the most significant human-induced alterations to the Earth's surface climate and can aggravate health risks for city dwellers during heat waves. Although the SUHI effect has received growing attention, its diurnal cycles (i.e., the variations over the full 24 h within the diel cycle) are poorly understood because polar-orbiting satellites (e.g., Landsat Series, Sentinel, Terra, Aqua) only provide one or two observations over each repeat cycle (e.g., 16 days) with constant overpass time for the same area. Geostationary satellites provide high-frequency land surface temperature (LST) observations throughout the day and the night, and thereby offer unprecedented opportunities for exploring the diurnal cycles of SUHI. Here we examined how the SUHI intensity varied over the course of the diurnal cycle in the Boston Metropolitan Area using LST observations from the NOAA's latest generation of Geostationary Operational Environmental Satellites (GOES-R). GOES-R LST was strongly correlated with MODIS LST (R2 = 0.98, p < 0.0001) across urban core, suburban, and rural areas. We calculated the SUHI intensity at an hourly time step for both the urban core and suburban areas using GOES-R LST data. The maximum SUHI intensity for the urban core occurred near noon, and was +3.0 °C (12:00), +5.4 °C (12:00), +4.9 °C (11:00), and +3.7 °C (12:00) in winter, spring, summer, and autumn, respectively. The maximum intensity for the suburban area was about 3.0 °C lower in spring and summer and 2.0 °C lower in autumn and winter than that of the urban-core area. The minimum SUHI intensity occurred at nighttime, and ranged from −1.0 °C to +1.0 °C. The difference in the nighttime SUHI intensity between urban core and suburban area was insignificant for all seasons except the summer. The SUHI intensity showed similar diurnal variations across the seasons. Throughout the year, the maximum SUHI intensity (+2.7–+5.8 °C) at the urban core occurred at 11:00–14:00 (local time), while the minimum SUHI intensity (−0.6–+0.9 °C) was commonly observed at 00:00–07:00 and 17:00–23:00. We also found different relationships between SUHI intensity and potential drivers within a diurnal cycle, characterized by the strongest correlation with impervious surface area and population size during the middle of the day, and with tree canopy cover at night. Our research highlights the great potential of the new-generation geostationary satellites in revealing the detailed diurnal variations of SUHI. Our findings have implications for informing urban planning and public health risk management.

Energy, water and carbon exchanges in managed forest ecosystems: description, sensitivity analysis and evaluation of the INRAE GO+ model, version 3.0

Abstract. The mechanistic model GO+ describes the functioning and growth of managed forests based upon biophysical and biogeochemical processes. The biophysical and biogeochemical processes included are modelled using standard formulations of radiative transfer, convective heat exchange, evapotranspiration, photosynthesis, respiration, plant phenology, growth and mortality, biomass nutrient content, and soil carbon dynamics. The forest ecosystem is modelled as three layers, namely the tree overstorey, understorey and soil. The vegetation layers include stems, branches and foliage and are partitioned dynamically between sunlit and shaded fractions. The soil carbon submodel is an adaption of the Roth-C model to simulate the impact of forest operations. The model runs at an hourly time step. It represents a forest stand covering typically 1 ha and can be straightforwardly upscaled across gridded data at regional, country or continental levels. GO+ accounts for both the immediate and long-term impacts of forest operations on energy, water and carbon exchanges within the soil–vegetation–atmosphere continuum. It includes exhaustive and versatile descriptions of management operations (soil preparation, regeneration, vegetation control, selective thinning, clear-cutting, coppicing, etc.), thus permitting the effects of a wide variety of forest management strategies to be estimated: from close to nature to intensive. This paper examines the sensitivity of the model to its main parameters and estimates how errors in parameter values are propagated into the predicted values of its main output variables.The sensitivity analysis demonstrates an interaction between the sensitivity of variables, with the climate and soil hydraulic properties being dominant under dry conditions but the leaf biochemical properties being most influential with wet soil. The sensitivity profile of the model changes from short to long timescales due to the cumulative effects of the fluxes of carbon, energy and water on the stand growth and canopy structure. Apart from a few specific cases, the model simulations are close to the values of the observations of atmospheric exchanges, tree growth, and soil carbon and water stock changes monitored over Douglas fir, European beech and pine forests of different ages. We also illustrate the capacity of the GO+ model to simulate the provision of key ecosystem services, such as the long-term storage of carbon in biomass and soil under various management and climate scenarios.

On the potential of variational calibration for a fully distributed hydrological model: application on a Mediterranean catchment

Abstract. Calibration of a conceptual distributed model is challenging due to a number of reasons, which include fundamental (model adequacy and identifiability) and algorithmic (e.g., local search vs. global search) issues. The aim of the presented study is to investigate the potential of the variational approach for calibrating a simple continuous hydrological model (GRD; Génie Rural distributed involved in several flash flood modeling applications. This model is defined on a rectangular 1 km2 resolution grid, with three parameters being associated with each cell. The Gardon d'Anduze watershed (543 km2) is chosen as the study benchmark. For this watershed, the discharge observations at five gauging stations, gridded rainfall and potential-evapotranspiration estimates are continuously available for the 2007–2018 period at an hourly time step. In the variational approach one looks for the optimal solution by minimizing the standard quadratic cost function, which penalizes the misfit between the observed and predicted values, under some additional a priori constraints. The cost function gradient is efficiently computed using the adjoint model. In numerical experiments, the benefits of using the distributed against the uniform calibration are measured in terms of the model predictive performance, in temporal, spatial and spatiotemporal validation, both globally and for particular flood events. Overall, distributed calibration shows encouraging results, providing better model predictions and relevant spatial distribution of some parameters. The numerical stability analysis has been performed to understand the impact of different factors on the calibration quality. This analysis indicates the possible directions for future developments, which may include considering a non-Gaussian likelihood and upgrading the model structure.

Large-scale estimation of buildings’ thermal load using LiDAR data

Increasing population and urbanisation threaten sustainable urban development due to increased resource consumption and emissions. As buildings are one of the largest energy consumers, it is crucial that their thermal load can be inspected on a large scale and at the highest resolution possible. The proposed method is performed in two stages. First, the LiDAR data and buildings’ metadata are preprocessed to generate high-resolution 3D building models that are represented by a triangle mesh. Thermal load of buildings throughout the year is then calculated per-triangle in a parallelised manner, while considering local micro-climate and shadowing from surroundings. Parallel design of the estimation enables significant speed-up of large-scale workloads, while maintaining accurate shadowing estimation. In experiments, the method was applied over a part of the city of Maribor, where heating and cooling loads were inspected in addition to other factors of thermal load estimation. Yearly thermal load calculation with an hourly time-step for 4,817 buildings with over 9.17 million triangles took about 8 min on a modern GPU. When comparing the run-times using a GPU and a modern CPU, the GPU was more than 60-times faster than a CPU for a million triangles. The speed-up grew with the number of triangles.

Modelling evapotranspiration in off-line simulations of greenhouse climate control

The design of greenhouse environmental control involves simulations under various climate and system configuration alternatives. These off-line model-based simulations cannot use online measured greenhouse conditions (temperature, humidity) and must rely on historical weather data and on well-established greenhouse and crop properties. An important element of such simulations is the partitioning of the dissipated energy into sensible and latent heat, which can be achieved in different ways. Specifically, the rate of evapotranspiration (ET) may be obtained from 1) knowledge of the aerodynamic and stomatal canopy resistances, 2) estimating ET from atmospheric conditions and control settings, or 3) estimating the Bowen (sensible to latent heat) ratio in the ventilation stream (VBR) from the atmospheric conditions. The objective of this study is to compare the simulated behaviour of a tomato greenhouse, based on these three quite different approaches, both among themselves and against a full-year data set from a commercial Dutch cherry-tomato greenhouse. Using a quasi-steady-state simulation-optimization program with hourly time steps, trajectories of indoor conditions (temperature, humidity, CO2 concentration), control actions (heating, ventilation, CO2 enrichment, thermal screen), yield and ET were obtained, and their monthly averages presented. At the monthly time scale the simulated results are very similar among themselves and to those observed, with the exception that the simulated summer CO2 concentrations, and hence fruit yield, are higher than those observed, possibly due to the optimization inherent in the simulations. The considerable VBR discrepancy between the three ET formulations during autumn is due to low nighttime ET, but this has a negligible effect on the daily calculated ET. In conclusion, it seems that for off-line design purposes a simple linear dependence of ET on global radiation is sufficient. Adaptation of the model to existing facilities may require a more refined version of the VBR approach.

BLIGHTSIM: A New Potato Late Blight Model Simulating the Response of Phytophthora infestans to Diurnal Temperature and Humidity Fluctuations in Relation to Climate Change.

Temperature response curves under diurnal oscillating temperatures differ from those under constant conditions for all stages of the Phytophthora infestans infection cycle on potatoes. We developed a mechanistic model (BLIGHTSIM) with an hourly time step to simulate late blight under fluctuating environmental conditions and predict late blight epidemics in potato fields. BLIGHTSIM is a modified susceptible (S), latent (L), infectious (I) and removed (R) compartmental model with hourly temperature and relative humidity as driving variables. The model was calibrated with growth chamber data covering one infection cycle and validated with field data from Ecuador. The model provided a good fit to all data sets evaluated. There was a significant interaction between average temperature and amplitude in their effects on the area under the disease progress curve (AUDPC) as predicted from growth chamber data on a single infection cycle. BLIGHTSIM can be incorporated in a potato growth model to study effects of diurnal temperature range on late blight impact under climate change scenarios.

Application of deep learning for solar irradiance and solar photovoltaic multi-parameter forecast

ABSTRACT In this study, the use of artificial neural network (ANN) models for solar irradiance and solar PV parameters forecasting in Nigeria is presented. Although solar irradiance prediction exists in literatures, the use of ANN for solar PV parameter prediction has not been considered in previous studies. Six different locations are selected in Nigeria and data from these locations have been used to train/test the ANN models developed. This study aims to model ANN algorithms that can forecast solar irradiance and solar PV parameters based on an hourly time-step more accurately. A deep learning regression model built on Levenberg-Marquardt back propagation algorithm is used to train and test the model for all the locations considered. Four different ANN models were developed for each location in Keras python using all the input parameters. The evaluation metrics used in this study are; R, R-squared, RMSE, and MAE. The models developed are capable of predicting solar irradiance and solar PV parameters. The R values for the ANN models range from 0.9046–0.9777 for solar irradiance and 0.7768–0.8739 for solar PV multi-parameters prediction.

Optimal Utilization of Light Energy in Semiclosed Greenhouse Using Three-Dimensional Cucumber Model

In the east of China, low temperature and light energy in winter are the main factors for the decline in cucumber yield, as well as in greenhouses without supplementary light. Optimal utilization of light energy is critical to increase cucumber yield. In this study, experimental measurements were conducted in two scenarios, April to May (Apr-May) and November to December (Nov-Dec) 2015, respectively, to analyze leaf development, dry matter accumulation, and yield of cultivated cucumber. Statistical analysis showed that leaves grew in Nov-Dec had larger leaf area and lower dry matter than leaves grew in Apr-May. This revealed that the dry matter accumulation rate per unit area was lower in winter. To be precise, the yield 0.174 kg/m2 per day in Nov-Dec was 35.3% lower than the yield in Apr-May. Environmental monitoring data showed that there was no significant difference in the average temperature between two scenarios, but the light intensity in Nov-Dec was only 2/3 of that in Apr-May. Three-dimensional (3D) cucumber canopy models were used in this study to quantify the effects of weak light on dry matter production in Nov-Dec. Three 3D canopies of cucumber were reconstructed with 20, 25, and 30 leaves per plant, respectively, by using a parametric modeling method. Light interception of three canopies from 8:00 to 15:00 on 4 November 2015 was simulated by using the radiosity-graphic combined model (RGM) with an hourly time step. CO2 assimilation per unit area was calculated using the FvCB photosynthetic model. As a result, the effects of light intensity and CO2 concentration on the photosynthetic rate were considered. The leaf photosynthesis simulation result showed that during the daytime in winter, the RuBP regeneration-limited assimilation Aj was always less than the Rubisco-limited assimilation Ac. This means that the limiting factor affecting the photosynthesis rate in winter was rather light intensity. As the CO2 concentration in the greenhouse was utmost in the morning, increasing the light intensity and therefrom increasing the canopy light interception at this time will be highly beneficial to increase the yield production. Through a comparative analysis of photosynthetic characteristics in these three virtual 3D canopies, the 25-leaf canopy was the best-performed canopy structure in photosynthetic production in winter. This study provides an insight into the light deficiency for yield production in winter and a solution to make optimal use of light in the greenhouse.