Real Weather Data Research Articles

Drone scheduling model for delivering small parcels to remote islands considering wind direction and speed

The logistics infrastructure in urban areas has been gradually strengthened; however, delivery demand on small islands or in mountainous regions might not be met in a timely manner because of intrinsic geographical conditions. However, with the rapid development of drone-related technologies (i.e., small unmanned aerial vehicles), drone-based parcel delivery is considered a viable alternative for delivery to such areas. In this paper, we propose a drone scheduling model to maximize the minimum delivery amount to the target islands. In particular, a robust optimization is adopted to consider the uncertainties of wind direction and speed. To capture the impact of wind on the performance of the proposed scheduling models, we conducted a sensitivity analysis. Moreover, we performed validation experiments with real weather data to determine the feasibility of the proposed models and cross-check the sensitivity analysis results. The results indicate that the proposed robust optimization-based approach achieves better performance than the deterministic model under strengthening wind conditions. Stronger wind corresponds to a better performance of the proposed model. Also, a cost analysis was conducted to support the usage of drones for parcel delivery to remote islands, and it showed that the longer the drone-based delivery is operated, the more operation costs can be saved in comparison with the current practice.

An Advanced MPPT Scheme for Standalone Solar powered PV System using Neuro-Fuzzy Estimator based on measured data

This paper deals with the problem of the optimization of the power, delivered by the photovoltaic panel (PVP). To achieve this aim, a neuro-fuzzy estimator (NFE), followed by a conversion coefficient and a calculation stage of the optimal duty cycle, has been developed. The NFE is used to calculate the open circuit voltage corresponding to each solar radiation, based only on the standard open circuit voltage. A coefficient, determining for each climatic condition the voltage of the maximum power directly from the open circuit voltage, is estimated by a measured test. Finally, the optimal duty cycles, next, determined by the input/output equation of boost converter. The system performance, under different scenarios, has been checked carrying out MATLAB simulations, using an existing photovoltaic model and real weather data, and comparing the simulation results with the measured one. The results demonstrate the effectiveness of the present approach. The efficiency of the proposal maximum power point tracking (MPPT) is proved and it showed that this controller can generate almost 99% of the real PVP maximum power.

Stochastic simulation of rain intrusion through small defects due to water rivulet overpressure. Introducing a driving rain leakage potential

There is a need of upgrading the old building stock with respect to the thermal insulation of the building envelope and specifically the façades. There are several systems on the market, and some are quite new and innovative. To bring down the cost some of the systems many are based on prefabricated moisture tight insulated units. This means that in case there is moisture tight barrier on the interior side, two moisture tight barriers surround the wall structure. The leakage of driving rain into the structure then represents a major threat to the durability of these systems. This paper investigates the pressure build up in water rivulets running down a façade acting together with the wind pressure. A driving rain leakage potential is introduced. Using real weather data years and Monte Carlo Simulations, the mean and standard deviation of the annual leakage through small hole is estimated. The examples show that the leakage can reach a level 0-0.5 liter/year for a hole with a diameter of 1-2 mm, and 0.5-3 liter/year for a diameter of 3-4 mm.

An Evaluation of Useful Daylight Illuminance in an Office Room with a Light Shelf and Translucent Ceiling at 51° N

The present study investigates the issue of computer-aided daylight evaluation in an office room with a light shelf and dropped translucent ceiling. In this type of room, daylight is admitted from two sources: (i) a standard window in the wall and (ii) via a light shelf and clerestory window, which illuminate the plenum located above the working space. The light from the plenum is transmitted through the translucent ceiling into the office room. The present study is based on data obtained through a computer-aided daylight simulation by DeLuminæ (DL-Light, ver. 11.0.9, and DL-Instant, ver. 6.1.4) software using the Radiance engine and real weather data for Wroclaw, Poland, at 51st lat. N. An office room of 12 × 6 m with different shading and daylight distribution scenarios was simulated (Variants 1–5). Next, the useful daylight illuminance (UDI (%)) for the range of 300–3000 lx and daylight glare probability (DGP) were calculated. To further optimize the daylighting scenarios, an adaptive shading system was simulated, which was activated when the illuminance value dropped below 300 lx. In the final variant, Variant 6, mean UDI300–3000 values were recorded to be above 80% for 95% of the area of the work plane. This allows the conclusion that a light shelf and translucent ceiling guide daylight deep into the room, improving uniformity and reducing glare when the standard window is covered by an adaptive shading system.

An IoT based Low-cost Weather Monitoring System for Smart Farming

Background: Weather monitoring is an important aspect of crop cultivation for reducing economic loss while increasing productivity. Weather is the combination of current meteorological components, such as temperature, wind direction and speed, amount and kind of precipitation, sunshine hours and so on. The weather defines a time span ranging from a few hours to several days. The periodic or continuous surveillance or the analysis of the status of the atmosphere and the climate, including parameters such as temperature, moisture, wind velocity and barometric pressure, is known as weather monitoring. Because of the increased usage of the internet, weather monitoring has been upgraded to smart weather monitoring. The Internet of Things (IoT) is one of the new technology that can help with many precision farming operations. Smart weather monitoring is one of the precision agriculture technologies that use sensors to monitor correct weather. The main objective of the research is to design a smart weather monitoring and real-time alert system to overcome the issue of monitoring weather conditions in agricultural farms in order for farmers to make better decisions. Methods: Different sensors were used in this study to detect temperature and humidity, pressure, rain, light intensity, CO2 level, wind speed and direction in an agricultural farm and real time clock sensor was used to measured real time weather data. The major component of this system was an Arduino Uno microcontroller and the system ran according to a program written in the Arduino Uno software. Result: This is a low-cost smart weather monitoring system. This system’s output unit were a liquid crystal display and a GSM900A module. The weather data was displayed on a liquid crystal display and the GSM900A module was used to send the data to a mobile phone. This smart weather station was used to monitor real-time weather conditions while sending weather information to the farmer’s mobile phone, allowing him to make better decisions to increase yield.

Optimal Sizing of Hybrid Wind-Solar Power Systems to Suppress Output Fluctuation

Harnessing wind energy is one of the fastest-growing areas in the energy industry. However, wind power still faces challenges, such as output intermittency due to its nature and output reduction as a result of the wake effect. Moreover, the current practice uses the available renewable energy resources as a fuel-saver simply to reduce fossil-fuel consumption. This is related mainly to the inherently variable and non-dispatchable nature of renewable energy resources, which poses a threat to power system reliability and requires utilities to maintain power-balancing reserves to match the supply from renewable energy resources with the real-time demand levels. Thus, further efforts are needed to mitigate the risk that comes with integrating renewable resources into the electricity grid. Hence, an integrated strategy is being created to determine the optimal size of the hybrid wind-solar photovoltaic power systems (HWSPS) using heuristic optimization with a numerical iterative algorithm such that the output fluctuation is minimized. The research focuses on sizing the HWSPS to reduce the impact of renewable energy resource intermittency and generate the maximum output power to the grid at a constant level periodically based on the availability of the renewable energy resources. The process of determining HWSPS capacity is divided into two major steps. A genetic algorithm is used in the initial stage to identify the optimum wind farm. A numerical iterative algorithm is used in the second stage to determine the optimal combination of photovoltaic plant and battery sizes in the search space, based on the reference wind power generated by the moving average, Savitzky–Golay, Gaussian and locally weighted linear regression techniques. The proposed approach has been tested on an existing wind power project site in the southern part of the Sultanate of Oman using a real weather data. The considered land area dimensions are 2 × 2 km. The integrated tool resulted in 39 MW of wind farm, 5.305 MW of PV system, and 0.5219 MWh of BESS. Accordingly, the estimated cost of energy based on the HWSPS is 0.0165 EUR/kWh.

Defrosting of Air-Source Heat Pumps: Effect of Real Temperature Data on Seasonal Energy Performance for Different Locations in Italy

In this paper, dynamic simulations of the seasonal coefficient of performance (SCOP) of Air-Source Heat Pumps will be presented by considering three different heat pump systems coupled with the same building located in three different Italian municipalities: S. Benedetto del Tronto (42°58′ North, 13°53′ East), Milan (45°28′ North, 9°10′ East), and Livigno (46°28′ North, 10°8′ East). Dynamic simulations were conducted by employing the software package TRNSYS and by considering real weather data (i.e., outdoor air temperature and humidity as well as solar radiation) referring to the three abovementioned cities for a period of 8 years (2013–2020) and collected from on-site weather stations. Attention has been paid to the modeling of the heat pump defrost cycles in order to evaluate their influence on the unit’s seasonal performance. Results show that, when referring to different years, the thermal energy demand displays huge variations (in some cases it can even double its value), while the effective SCOP is characterized by scarce variability. Sensible variations in SCOP values are achieved for Livigno.

Effects of Variable Weather Conditions on Baled Proportion of Varied Amounts of Harvestable Cereal Straw, Based on Simulations

All harvestable cereal straw cannot be collected every year in regions where wet periods are probable during the baling season, so some Swedish studies have used ’recovery coefficients’ to estimate potential harvestable amounts. Current Swedish recovery coefficients were first formulated by researchers in the early 1990s, after discussions with crop advisors, but there are no recent Swedish publications on available baling times and recovery proportions. Therefore, this study evaluated baling operations over a series of years for representative virtual farms and machine systems in four Swedish regions, to determine the available time for baling, baled straw ratio and annual variation in both. The hourly grain moisture content of pre-harvested cereals and swathed straw was estimated using moisture models and real weather data for 22/23 years, and the results were used as input to a model for simulating harvesting and baling operations. Expected available baling time during August and September was estimated to be 39–49%, depending on region, with large annual variation (standard deviation 22%). The average baling coefficient was estimated to be 80–86%, with 1400 t·year−1 harvestable straw and 15 t·h−1 baling capacity, and the annual variation was also considerable (s.d. 20%).

Stacking Ensemble Method with the RNN Meta-Learner for Short-Term PV Power Forecasting

Photovoltaic (PV) power forecasting urges in economic and secure operations of power systems. To avoid an inaccurate individual forecasting model, we propose an approach for a one-day to three-day ahead PV power hourly forecasting based on the stacking ensemble model with a recurrent neural network (RNN) as a meta-learner. The proposed approach is built by using real weather data and forecasted weather data in the training and testing stages, respectively. To accommodate uncertain weather, a daily clustering method based on statistical features, e.g., daily average, maximum, and standard deviation of PV power is applied in the data sets. Historical PV power output and weather data are used to train and test the model. The single learner considered in this research are artificial neural network, deep neural network, support vector regressions, long short-term memory, and convolutional neural network. Then, RNN is used to combine the forecasting results of each single learner. It is also important to observe the best combination of the single learners in this paper. Furthermore, to compare the performance of the proposed method, a random forest ensemble instead of RNN is used as a benchmark for comparison. Mean relative error (MRE) and mean absolute error (MAE) are used as criteria to validate the accuracy of different forecasting models. The MRE of the proposed RNN ensemble learner model is 4.29%, which has significant improvements by about 7–40%, 7–30%, and 8% compared to the single models, the combinations of fewer single learners, and the benchmark method, respectively. The results show that the proposed method is promising for use in real PV power forecasting systems.

Intelligent approach for optimal sizing in photovoltaic panel-battery system and optimizing smart grid energy

Renewable energy plays a very important role in solving energy problems, and solar energy is one of the most important renewable sources, especially in sunny countries. This paper deals with two problems: The first one is about optimal sizing in a photovoltaic panel (PVP)-battery system, and the second consists in energy management in smart grids. To achieve the first objective, an adaptive neuro-fuzzy inference system (ANFIS) estimation algorithm is developed in order to estimate a database of instantaneous photovoltaic power. The estimated instantaneous photovoltaic power is used in an optimal algorithm to size a PVP-battery power station to supply a 1.5 kW AC load. For the second objective, a deep learning forecasting algorithm is realized to estimate the smart grid parameters so as to optimize the consumption energy. All results are checked carrying out Matlab simulation using real weather data. The simulation results give a good performance of our proposed sizing and management systems.

The Impact of Reflectivity Gradients on the Performance of Range-Oversampling Processing

AbstractRange-oversampling processing is a technique that can be used to lower the variance of radar-variable estimates, reduce radar update times, or a mixture of both. There are two main assumptions for using range-oversampling processing: accurate knowledge of the range correlation and uniform reflectivity in the radar resolution volume. The first assumption has been addressed in previous research; this work focuses on the uniform reflectivity assumption. Earlier research shows that significant reflectivity gradients can occur in storms; we utilized those results to develop realistic simulations of radar returns that include effects of reflectivity gradients in range. An important consideration when using range-oversampling processing is the resulting change in the range weighting function. The range weighting function can change for different types of range-oversampling processing and some techniques, such as adaptive pseudowhitening, can lead to different range weighting functions at each range gate. To quantify the possible effects of differing range weighting functions in the presence of reflectivity gradients, we developed simulations to examine varying types of range-oversampling processing with two receiver filters: a matched receiver filter and a wider-bandwidth receiver filter (as recommended for use with range oversampling). Simulation results show that differences in range weighting functions are the only contributor to differences in radar reflectivity measurements. Results from real weather data demonstrate that the reflectivity gradients that occur in typical severe storms do not cause significant changes in reflectivity measurements, and the benefits from range-oversampling processing outweigh the possible isolated effects from large reflectivity gradients.

A new method for forecasting energy output of a large-scale solar power plant based on long short-term memory networks a case study in Vietnam

This paper proposes a new model for short-term forecasting power generation capacity of large-scale solar power plant (SPP) in Vietnam considering the fluctuations of weather factors when applying the Long Short-Term Memory networks (LSTM) algorithm. At first, a configuration of the model based on the LSTM algorithm is selected in accordance with the weather and operating conditions of SPP in Vietnam. Not only different structures of LSTM model but also other conventional forecasting methods for time series data are compared in terms of error accuracy of forecast on test data set to evaluate the effectiveness and select the most suitable LSTM configuration. The most suitable configuration has been selected and applied on Thanh Thanh Cong No 1 (TTC) SPP with 2 input cases: real historical weather data and forecasted weather data. The results show that second case gives a much larger Mean Absolute Percentage Error (MAPE) than that of first case (10.857% versus 3.491%). Based on above experiment, new additional features are proposed to improve the selected LSTM model precision and cope with the problem of error due to weather forecast data. The result of the application of the new prediction model for TTC solar plant indicates that the MAPE is reduced from 10.857% to 9.881%.

Improvement of techno-economic optimal sizing of a hybrid off-grid micro-grid system

This paper deals with an improvement of a techno-economic optimal sizing of a hybrid off-grid micro-grid system in order to minimize the cost of produced energy and the reliability level required by customers. The used iterative approach is based on a recursive algorithm in combination with robust energy management. In fact, to study the system reliability, the Deficiency of Power Supply Probability (DPSP) is used. For the cost of the system study, two economic criteria are used; the total net present cost and the energy cost. In the literature, this approach has been widely used. However, the unpredictable weather circumstances, such as the irradiation conditions and/or low wind speed, present the most drawbacks of renewable energy. In our proposed paper, the sizing of two micro-grid system configurations ((1) a Photovoltaic Panel (PVP) and a battery, and (2) a PVP, wind and a battery) is studied while considering the autonomy days. We measure the size of both systems without taking into account the autonomy days, where DPSP = 0. When we consider the autonomy days, the dimensioned battery becomes unable to cover the load demand, so DPSP≠0. Accordingly, it is difficult to ensure the power supply reliability of a system with insufficient capacity. Moreover, by increasing the number of the autonomy days (from 1 to 5 days), the system cost will increase. Actually, it becomes as 16 times and 6 times as the initial cost for the PVP and battery system and the PVP, wind and battery system, respectively. Thus, to overcome this aforementioned problem, we use a diesel generator as a secondary power supply thanks to its efficiency at covering the load demand and its low cost when compared to the battery capacity. The obtained results are verified carrying out Matlab simulation using real weather data.

An Evaluation of Annual Luminous Exposure from Daylight in a Museum Room with a Translucent Ceiling

The current study investigates the issue of computer-aided daylight evaluation in a museum room with a dropped translucent ceiling. In this type of room, daylight is admitted through classic windows located in the facade and then distributed in the plenum, which is located above the exhibition space and transmitted through the translucent ceiling into the museum room. This illumination method enables guiding daylight deep into the room, excluding the impact of direct solar radiation. The presented study is based on data obtained through computer-aided daylight simulation by DeLuminæ (DL-Light, ver. 11.0.9) software using the Radiance software for all calculations and real weather data for Wroclaw, Poland. A museum room of 12 × 12 m with three different heights of the plenums was simulated to establish an optimal relation of the width to height plenum ratio. Next, the annual exposure in K lx·h/year was calculated, as sensitive works of art may be subjected to damage caused by light exposure. To further reduce illumination, the simulation of an automatic shading system in the form of horizontal louvers was performed. The system was activated when certain illumination values were detected by the sensor on the building’s roof.

Two-stage stochastic programming formulation for optimal design and operation of multi-microgrid system using data-based modeling of renewable energy sources

Microgrid (MG) is intended to accommodate distributed renewable energy sources and manage the generated electricity using a dispatch and storage system to satisfy the demand of a local region over time. Main challenges associated with operating such a system stems primarily from the intermittent and uncertain nature of renewable energy sources causing the energy generation to be highly irregular and less predictable. Multi-microgrid (MMG) systems with flexibility to trade energy between individual MGs can alleviate the demand–supply mismatch problem, but its design and operation become more complicated. Previous studies on MMGs addressed only the case where all the MGs are fully connected to share electricity. However, the installation of electric cable connections over the full network can be costly and is often unjustified. To address the question of how to best achieve a tradeoff, this work proposes the use of a two-stage stochastic decision-making approach for designing and operating an MMG considering both the capital cost for installing the electric cables and operating cost. First, real weather data are used to develop stochastic models that describe the uncertain, intermittent nature of energy production through renewable sources over a day. Then, in the first stage, design-level decisions regarding the installation of the electric cable between individual pairs of the MGs are to be determined to achieve the best tradeoff for the modeled system and weather pattern. In the second stage, the optimal energy dispatch planning for each individual MG including the energy trading between the MGs is carried out based on a particular realized scenario of the renewable energy production.

Energy, exergy and economic (3E) evaluation of the photovoltaic/thermal collector-assisted heat pump domestic water heating system for different climatic regions in Turkey

In this study, the energy, exergy and economic (3E) analyses were performed for the photovoltaic/thermal collector-assisted heat pump domestic water heating system under two different climatic regions (Hakkari and Trabzon) in Turkey. Designed photovoltaic/thermal collector-assisted heat pump domestic water heating system with a storage tank having larger volume and cross-flow heat exchanger in Turkey can be considered as novelty of this study. The system modelled with the help of TRNSYS simulation code by using real weather data and energy analysis was performed based on year-round simulation results. And then, exergy and economic analyses were obtained based on monthly average temperatures. The system considered in this study was also compared economically with conventional natural gas and electricity water heating systems. According to the results, the maximum energy and annual average exergy efficiencies of the considered system were 68% and 22.3%, respectively, for Hakkari, and 67% and 21.4%, respectively, for Trabzon. Although Hakkari has cold climate conditions, the investigated system had lower energy consumption costs than conventional systems. The annual energy consumption costs of the system for Hakkari and Trabzon were calculated as 67.14$ and 135.75$, respectively. The results bring out that although the investment of the system is feasible for Hakkari, it is not feasible for Trabzon.

Predictive storage strategy for optimal design of hybrid CSP-PV plants with immersion heater

A hybrid solar power plant effectively combines the two main advantages of solar power plants: concentrated solar power (CSP) with a cheap thermal storage system and photovoltaic (PV) with cheap electricity production. In a hybrid plant, both systems are coupled with the thermal storage, where an immersion heater can transfer the PV energy into thermal energy. A real-time storage strategy is developed using model predictive control considering the future energy tariff and future weather conditions. The efficiency of the power block is considered as quadratic function in dependency of the bulb temperature. As strategy the optimization problem is formulated as linear program. The methods are tested in a realistic scenario for a hybrid CSP-PV power plant with real weather data and different tariffs. Furthermore, on the basis of the best strategy, the optimal design for CSP, PV and storage size is investigated. In comparison to the state of the art (heuristic) optimization we gain 14 % by using a predictive control strategy in combination with an optimal power plant configuration. We show that the storage strategy not only impacts the achievable plant output but also very strongly the subsystem sizing. It can be seen that the plant configuration is massively influenced by the storage control scheme.

Time-series prediction model of PM2.5 concentration based on LSTM neural network

LSTM (long-short term memory neural network) is a neural network suitable for simulating and predicting time series changes. This paper designs a PM2.5 concentration prediction model based on the LSTM neural network. Use Python language programming implementation under the Tensor Flow framework, training the model using real weather data from 2015-01-01∼2019-01-01, and the obtained model can accurately predict the concentration change of future PM2.5. RMSE and DC were used as evaluation indexes to compare the LSTM neural network model with the traditional BP (Back Propagation) algorithm model. It turns out, LSTM model has the characteristics of high precision and stable prediction effect, proved that the time series prediction ability of the LSTM model is more superior. As an effective method for predicting PM2.5 concentration data, it can provide the basis for predicting pollutants.

A novel approach in stand-alone photovoltaic system using MPPT controllers & NNE

An innovative technique for implementing the maximum power point tracking (MPPT) system for the Photo-Voltaic (PV) panel is proposed in this paper. The system comprises of a Neural Network Estimator (NNE), followed by a conversion coefficient and a calculation stage of the optimal duty cycle. The proposed NNE technique, implemented in MATLAB/Simulink, calculates the ratio of open circuit voltage corresponding to each solar radiation for various value of temperature to the corresponding standard open circuit voltage. A regularization coefficient is determined, which estimates the voltage corresponding to the maximum power directly from the open circuit voltage for each solar radiation. Finally, from the input/output equation of boost converter, the optimal duty cycle is evaluated. Simulation results have been used to assess the system performance for various situations obtained by using an existing photovoltaic model and real weather data, and relevant comparisons are also done to substantiate the performance advantages of the method. The Simulation results demonstrate that the optimization of the P&O MPPT control with a NN algorithm provides better results and performance in terms of accuracy and complexity. The results support the effectiveness and performance of using NNE-based MPPT controller approach. It is demonstrated that this controller can achieve almost 99% of the real PVP maximum power.

Multi-Time Scale Smoothed Functional With Nesterov’s Acceleration

Smoothed functional (SF) algorithm estimates the gradient of the stochastic optimization problem by convolution with a smoothening kernel. This process helps the algorithm to converge to a global minimum or a point close to it. We study a two-time scale SF based gradient search algorithm with Nesterov’s acceleration for stochastic optimization problems. The main contribution of our work is to prove the convergence of this algorithm using the stochastic approximation theory. We propose a novel Lyapunov function to show the associated second-order ordinary differential equations’ (o.d.e.) stability for a non-autonomous system. We compare our algorithm with other smoothed functional algorithms such as Quasi-Newton SF, Gradient SF and Jacobi Variant of Newton SF on two different optimization problems: first, on a simple stochastic function minimization problem, and second, on the problem of optimal routing in a queueing network. Additionally, we compared the algorithms on real weather data in a weather prediction task. Experimental results show that our algorithm performs significantly better than these baseline algorithms.