Real Weather Data Research Articles

Parameter Self-Tuning PID Control for Greenhouse Climate Control Problem

The difficulty of the greenhouse climate control is the great uncertainties of the weather and greenhouse climate. Although many control approaches have been proposed to solve this problem, the structures of the controllers are usually complex, and the reliability of the controllers is usually not good in practice. Therefore, to improve the reliability of the controllers, this article proposes a parameter self-tuning PID control approach (STPID). In this PID control scheme, three environmental outputs including the inside temperature, humidity and CO2 concentration are firstly transformed into four equivalent unit outputs, and the equivalent outputs are controlled by four PID controllers respectively. Thus, each PID controller and the corresponding equivalent output construct a single closed-loop system, and the original system can be decoupled. To ensure the desired control performance, the Levenberg-Marquart (LM) algorithm is introduced to tune the PID parameters. Based on the real weather data, a simulation was performed to illustrate the effectiveness of the proposed method, and a comparison study was done to observe the performance of the proposed method. The results indicate that control performance is good.

Effect of the ground heat storage on solar chimney power plant performance in the South of Tunisia: Case of Tozeur

In this paper, the ground heat storage effect on the performance of a SCPP implemented in the South of Tunisia, is investigated. First, a 3D CFD ANSYS-Fluent model is used to study the SCPP performance in the region of Tozeur, which represents the best location in the country, in terms of energy production for a SCPP. Such an installation with a geometry similar to the Manzanares plant, can produce averagely around 150 MWh/year of electrical energy. Subsequently, the study of the effect of the thermal storage on the SCPP performance is presented using an in-house code. In this model, a two-dimensional cylindrical domain is used for the transient conduction in the ground and 1D buoyancy driven flow for the air flow. Such a modeling approach is much more efficient that the full conjugate heat transfer CFD solution obtained through ANSYS-Fluent CFD software and enables the simulation of long term transient behavior. Moreover and for more realistic results, the model uses real weather data for wind speed, ambient temperature and solar radiation. With this model, the performance of three types of ground storage media are investigated. Results show that the ratio between the released and stored energies depends mainly on the season and the energy production is increased by about 35% compared to a system without storage. The results demonstrate the importance of including long-term thermal storage when modelling SCPP.

Performance investigations on a sensible heat thermal energy storage tank with a solar collector under variable climatic conditions

Due to the intermittent nature of the solar energy and the mismatch between the instantaneous demand and supply, solar energy could not be used effectively, or continuously, in solar-assisted applications such as heating of a building. As a common approach, it is possible to store the available solar energy when it is abundant and use it later to operate solar-assisted applications sustainably. Thermal energy storage (TES) applications provide critical solutions for ensuring the sustainability of solar energy. A proper TES tank should be designed in such a way to possess a high heat transfer rate, energy efficiency, and exergy efficiency during the charging and discharging processes. In the current work, a sensible heat TES tank that is integrated with a flat plate solar collector is considered. An in-house transient code is developed to evaluate spatial and temporal temperature variations within the storage tank and the solar collector throughout a day under variable weather conditions. The variation in working fluid temperature along the tank height is compared against the experimental and numerical results from the literature to ensure the validity of the code. The influences of the mass flow rate of heat transfer fluid, the diameter of the storage material and height of the TES tank on dimensionless performance measures, such as stratification number, energy, and exergy efficiencies, are numerically evaluated under real weather data for four months as November, December, January and February. In the proposed system, the storage medium temperatures vary in the range of 40–60 °C. The stored thermal energy can be used in a building in various aspects such as supplying warm water, underfloor space heating, or indirect heating with a heat pump system.

High Wave Predictive Numerical Simulation for the Wave Alarm System

Lee, Y.B.; Kim, K.H; Kim, H.D; Kim, J., Kwak, K., and Park, T., 2019. High wave predictive numerical simulation for the wave alarm system. In: Lee, J.L.; Yoon, J.-S.; Cho, W.C.; Muin, M., and Lee, J. (eds.), The 3rd International Water Safety Symposium. Journal of Coastal Research, Special Issue No. 91, pp. 91-95. Coconut Creek (Florida), ISSN 0749-0208.In recent times, coastal damage has been on the increase due to rising sea levels as a result of global warming and the increasing frequency of high waves as a result of climate change. High swells and waves directly invading the coastline propagate more energy onto the shoreline than preexisting waves, causing physical damages such as coastal erosion as well as human casualties. However, an alarm system that can predict high waves by analyzing weather forecasts and transmit the information to a user in real time does not yet exist. Such a technology is necessary to prevent damage and human loss to ever increasing high swells and waves. This research aims to successfully predict high waves by using real time weather data in its simulations to detect potential danger in a user's location, as part of the larger research for the development of a high wave alarm system technology.

Solar irradiance and temperature influence on the photovoltaic cell equivalent-circuit models

Various works investigated different photovoltaic (PV) cell equivalent-circuit models and several techniques were proposed to extract their unknown parameters. The present paper analyzes the current/voltage (I-V) characteristics for Si-crystalline PV modules under non-standard conditions of irradiance and temperature, by using single-diode and double-diode models. The Chaibi and Ishaque methods are employed to determine the parameters for each equivalent-circuit model. Then, the I-V curves provided by the manufacturers and the calculated I-V characteristics are compared at different levels of irradiance and temperature. The comparison suggests prioritizing one of the two equivalent-circuit models according to the prevailing meteorological inputs. As such, a hybrid approach is proposed in order to select the most appropriate model depending on the relevant climatic conditions. The presented approach accuracy is evaluated using real weather data of two PV plants located in two different climatic zones (Mediterranean and Semi-Continental). Results show that the double-diode model is more reliable for low-irradiance levels; however, the single-diode model performs well with low-temperature fluctuations. An error reduction of 53.93% and 21.04% can be reached for the cloudy weather and for the sunny days, respectively. Accordingly, this approach can be easily implemented as a computing tool to achieve more accurate prediction in the PV systems simulations.

Efficient frequency regulation in highly penetrated power systems by renewable energy sources using stochastic fractal optimiser

The study proposes efficient controllers to regulate frequency in highly penetrated power systems by renewable energy sources. Frequency controllers for variable speed wind turbines are designed to extract the stored energy in rotating masses, and efficiently regulate the pitch angle for frequency support. A temporary fast participation of storage battery incorporated with photovoltaic sources is provided via auxiliary controller. Controllers' fine tuning is realised by using stochastic fractal optimiser (SFO). The integral time absolute error in area frequency and tie line power represents the adapted objective function subjects to set of constraints. The performance assessments are carried out in three phases: (i) at initial stage, the secondary control is disabled and impact of wind penetration level on frequency nadir and frequency deviations are investigated, (ii) coordinated inertia/energy storage control is demonstrated, and (iii) finally, robustness analysis is made considering system parametric variations and real weather data. The proposed control strategy is verified by simulation in MATLAB/SIMULINK environment. The drawn numerical results by the SFO are compared with those achieved by genetic algorithm and the built-in controller tuner in SIMULINK. Performance assessments, comparative study along with robustness analysis of the SFO results confirm its viability and effectiveness.

Weather-Dependent Power Flow Algorithm for Accurate Power System Analysis Under Variable Weather Conditions

Accurate power flow analysis is essential to system operators for planning, design, analysis, and control of power networks. The accuracy of power flow analysis can be increased significantly by including the weather-dependent characteristics of the system. In this paper, a novel weather-dependent power flow algorithm is proposed and studied in comparison to the very well-known conventional power flow. The weather-dependent power flow algorithm is novel in the sense that it is explicitly parameterized in terms of typically available measured weather parameters (ambient temperature, solar irradiance, wind speed, and wind angle) to perform a fully coupled weather-dependent power flow analysis. Using this algorithm, the IEEE 30-bus power network was studied utilizing real weather data by performing three year-long steady-state time-series power flow analyses. The study demonstrates that the proposed weather-dependent power flow algorithm accurately estimates the branch resistances, the system states (current and voltages), the power losses, the branch flows, and the branch loadings. These are made possible because the proposed algorithm accurately estimates branch conductor temperature due to the coupling of power flow with the nonlinear heat balance model. An analysis of the computational complexity of the proposed algorithm is also presented.

Exposure response function for a quantitative prediction of weathering caused aging of polyethylene

Abstract The exposure response function of the carbonyl formation over the bulk has been determined for a high-density polyethylene of a thickness of 200 μm, which was used as a weathering reference material according to ISO TR 19032. To this end, spectral sensitivity was studied by local measurement of the effect of spectrally dispersed irradiation. Both the exposure device and the methodology of determination are described. The temperature dependency of photooxidation was determined by UV exposure at various temperatures between 23 and 80 °C. Deviations from linearity and thus reciprocity below 40 °C are discussed and assumed to be related to diffusion limitations. An Arrhenius approach – based on data of linear carbonyl formation – has been incorporated into the exposure response function. Using this exposure response function, aging in terms of the distribution of a quantitative property change over a plastic component can be predicted for a specific outdoor location with real chronologic weather data as input for the exposure. Thus, artificial and natural weathering can be linked and compared. The established exposure response function has been validated by outdoor exposure results from the literature. If an estimated diffusion limitation is taken into consideration, calculations and published data are in good agreement.

Reactive Power Cost From PV Inverters Considering Inverter Lifetime Assessment

Photovoltaic (PV) system inverters have been frequently utilized for reactive power support in the literature. Although the benefits of PV reactive power for the grid have been quantified, the costs incurred by the PV owner have largely been ignored. This paper combines the findings from power electronics research and power system economics to formulate the cost of reactive power from PV inverters, considering the inverter degradation due to the reactive power provision. One-year simulations with real weather and load data for the case of Singapore for different levels of PV penetration have been conducted. Subsequently, the benefits of local reactive power provision to the system and its effect on the lifetime of PV inverters were analyzed. It was found that the cost of inverter lifetime reduction is a significant part of the reactive power cost (more than 50% at lower PV penetration), but decreases at higher PV penetration when the reactive power support is distributed over more PV systems. Through comprehensive analysis, the feasible range for monetary incentives for PV reactive power support has been identified. The results from this paper can, therefore, be used as a foundation for future research, formulating regulations, and for market analysis.

Semantic-aware aircraft trajectory prediction using flight plans

Aircraft trajectory prediction (TP) is a challenging and inherently data-driven time-series modeling problem. Adding annotation or enrichment parameters further increases the search space complexity, especially when ‘blind’ optimization algorithms are employed. In this paper, flight plans, localized weather and aircraft properties are introduced as trajectory annotations that enable modeling in a space higher than the typical 4-D spatio-temporal. A multi-stage hybrid approach is employed for a new variation of the core TP task, the so-called Future Semantic Trajectory Prediction, including clustering the enriched trajectory data using a semantic-aware similarity function as distance metric. Subsequently, a separate predictive model is trained for each cluster, using a nonuniform graph-based grid that is formed by the waypoints of each flight plan. In practice, flight plans constitute a constrained-based training of each predictive model, one for each waypoint, independently. The proposed method is formulated and experimentally validated with real aviation dataset (flight plans and IFS radar tracks) and localized weather data for a 1-month time frame of flights in the Spanish airspace. Various types of predictive models are tested, including hidden Markov model (HMM), linear regressors, regression trees and feed-forward neural networks. The results show very narrow confidence intervals for the per-waypoint TP errors in HMM, while the more efficient linear and nonlinear regressors exhibit 3-D spatial accuracy much lower than the current state of the art, up to a factor of five compared to ‘blind’ TP for complete flights, in the order of 2–3 km compared to the actual flight routes.

Global Maximum Power Point Tracking under Shading Condition and Hotspot Detection Algorithms for Photovoltaic Systems

Photovoltaic (PV) technology has been gaining an increasing amount of attention as a renewable energy source. Irradiation and temperature are the two main factors which impact on PV system performance. When partial shading from the surroundings occurs, its incident shadow diminishes the irradiation and reduces the generated power. Moreover, shading affects the pattern of the power–voltage (P–V) characteristic curve to contain more than one power peak, causing difficulties when developing maximum power point tracking. Consequently, shading leads to a hotspot in which spreading the hotspot widely on the PV panel’s surface increases the heat and causes damage to the panel. Since it is not possible to access the circuit inside the PV cells, indirect measurement and fault detection methods are needed to perform them. This paper proposes the global maximum power point tracking method, including the shading detection and tracking algorithm, using the trend of slopes from each section of the curve. The effectiveness was confirmed from the dynamic short-term testing and real weather data. The hotspot-detecting algorithm is also proposed from the analysis of different PV arrays’ configuration, which is approved by the simulation’s result. Each algorithm is presented using the full mathematical equations and flowcharts. Results from the simulation show the accurate tracking result along with the fast-tracking response. The simulation also confirms the success of the proposed hotspot-detection algorithm, confirmed by the graphical and numerical results.

NSGA-II and MOPSO based optimization for sizing of hybrid PV/wind/battery energy storage system

<span lang="MS">This paper presents a Stand-alone Hybrid Renewable Energy System (SHRES) as an alternative to fossil fuel based generators. The Photovoltaic (PV) panels and wind turbines (WT) are designed for the Malaysian low wind speed conditions with battery Energy Storage (BES) to provide electric power to the load. The appropriate sizing of each component was accomplished using Non-dominated Sorting Genetic Algorithm (NSGA-II) and Multi-Objective Particle Swarm Optimization (MOPSO) techniques. The optimized hybrid system was examined in MATLAB using two case studies to find the optimum number of PV panels, wind turbines system and BES that minimizes the Loss of Power Supply Probability (LPSP) and Cost of Energy (COE). The hybrid power system was connected to the AC bus to investigate the system performance in supplying a rural settlement. Real weather data at the location of interest was utilized in this paper. The results obtained from the two scenarios were used to compare the suitability of the NSGA-II and MOPSO methods. The NSGA-II method is shown to be more accurate whereas the MOPSO method is faster in executing the optimization. Hence, both these methods can be used for techno-economic optimization of SHRES. </span>

Phase Change Material Wallboard (PCMW) melting temperature optimisation for passive indoor temperature control

Incorporating PCMs into traditional building envelops is an effective way to minimise indoor temperature fluctuations and maximise thermal comfort in summertime. PCMW is of particular interest due to its ease of installation. This paper presents a theoretical analysis in which both outdoor air temperature and solar radiation are considered to predict the optimal melting temperature of an internal PCMW for passive indoor temperature control. Key factors influencing the selection of optimal melting temperature are studied including outdoor temperature, solar radiation, window size, air change rate (ACH), and external wall and window U-values. A case study was carried out on a lightweight building using real weather data for the summer months in the UK. The simulation was developed in EnergyPlus with the potential to reduce energy use while maintain the desired internal temperatures determined. The optimal melting temperature was found to be 23.4 °C during summer in the UK from a theoretical analysis, which showed good agreement with the numerical simulations performed. In summer, the percentage energy savings can be as high as 40% compared with a similar building without PCMWs and the period which temperatures are maintained within the thermal comfort range can be improved by up to 7.2%.

Feasibility analysis for a novel dew point air cooler applied in warm and humid climate: a case study in Beijing

The air humidity and temperature greatly impact the water evaporation efficiency. It is commonly doubted whether the evaporative cooling technology is suitable for the humid climate as well as the public’s care about the water consumption for cooling. This paper provids a feasibility study for the use of a DPC (dew point air cooler) in Beijing, a representative city in warm and humid climate. The overall analyses include an hourly cooling performance analysis in a typical cooling day, annual energy performance prediction and economic analysis based on an experimentally validated simulation tool. With detailed analyses of the real weather data all year round, it is found the outdoor air must be dehumidified to meet the humidity requirement in most of the cooling hours for any air conditioning means. Thus, the superiority of evaporative cooling would be prominent comparing to the conventional mechanical cooling. The water consumption is little especially considering the fact that the conventional power generation consumes large quantity of water and the DPC saves large sales of power. The results proved the feasibility for the DPC applied in Beijing. The success in application of the proposed DPC in the warm and humid region promises more opportunities of the dew point cooling technology.

Evaluation of the power system reliability if a nuclear power plant is replaced with wind power plants

The modern power systems include a larger number of more dispersed and smaller power generating sources, which are slowly replacing larger and more concentrated power sources. The objective of this paper is to analyse the replacement of nuclear power plant with wind power plants to compare both cases from the viewpoints of power system reliability. The load diagram and the variability of the power plants, whose power depend on the environmental parameters with the time is considered. This paper is focused on the loss of load expectation. The upgraded method considers plant power as a function of the time instead of its nominal power. Initial real power system model consists of twelve power plants including one nuclear power plant. This plant is then replaced by three wind power plants with their total power five times larger than the power of the nuclear power plant. A comparison of reliability in terms of comparing the loss of load expectation is performed using the real weather data for one calendar year. The results show that the replacement of the nuclear power plant with several wind power plants with wind power capacity five times larger than the nuclear power capacity causes a decrease of the power system reliability.

Partial Shading Detection and Global Maximum Power Point Tracking Algorithm for Photovoltaic with the Variation of Irradiation and Temperature

Photovoltaic (PV) technology has been the focus of interest due to its nonpolluting operation and good installation flexibility. Irradiation and temperature are the two main factors which impact the performance of the PV system. Accordingly, when partial shading from surroundings occurs, its incident shadow diminishes the irradiation and reduces the generated power. Since the conventional maximum power point tracking methods (MPPT) could not distinguish the global maximum power of the power-voltage (P-V) characteristic curve, a new tracking method needs to be developed. This paper proposes a global maximum power point tracking method using shading detection and the trend of slopes from each section of the curve. Full mathematical equations and algorithms are presented. Simulations based on real weather data were performed both in short-term and long-term studies. Moreover, this paper also presents the experiment using the DC-DC synchronous and interleaved boost converter. Results from the simulation show an accurate tracking result and the system can enhance the total energy generated by 8.55% compared to the conventional scanning method. Moreover, the experiment also confirms the success of the proposed tracking algorithm.

Retrofit solutions for an historic building integrated with geothermal heat pumps

Since the beginning of this century, according to EPBD Directive, Italian legislation has been focusing on existing buildings’ retrofit, as they represent a large part of the national building stock. In this context, the most difficult challenge is preserving heritage and obtaining energy saving at the same time, which is the aim of the European Standard EN 16883 published in 2017. Currently there are few energy efficiency actions that can be applied to improve the energy performance of these buildings, due to the strict regulation on ‘protected status’. The case study presented in this paper gives an overview of refurbishment solutions that can get around Italian legislation for one of the oldest heritage buildings in Padova. The building complex that can be seen nowadays is the result of the grouping of several surrounding buildings, renaissance palaces and medieval towers. Energy simulations have been implemented using EnergyPlus software. The thermal behaviour of the building has been investigated considering the peculiarities of the envelope and real weather data. Internal loads and generation system schedules have been estimated based on the information obtained from the system monitoring the energy consumption. Envelope and system retrofit have been considered.

The effect of phase change material incorporated building wall on the CO<SUB align="right">2 mitigation: a case study of Izmir, Turkey

Buildings are considered to be one of the considerable energy consuming systems and greenhouse gas emissions sources, and 14% of this energy is used for cooling. This paper evaluates the energetic benefits and CO2 emissions of incorporating phase change materials (PCMs) on the facade of an office building under real weather data conditions for Izmir, Turkey for cooling. For this purpose, two situations, specifically the effect of the melting temperature (27°C, 32°C and 37°C) and the location of PCM (interior, middle and exterior of the wall) have been investigated for five months, from May to September. The results indicate that integration of PCM with a melting temperature of 32°C at the interior section of the wall in the hottest months of the year, namely June, July, and August reduce the related CO2 emissions by 50.15, 122.339 and 121.727 kg CO2 respectively, compared to the baseline configuration without PCM.

Development of a Stochastic Storm Generator Using High-Resolution Precipitation Records

Abstract.Sophisticated field and watershed scale environmental models for runoff, erosion control, environmental, and global-change investigations require detailed continuous temporal and spatial inputs of precipitation to drive the hydrologic processes. For accurate estimates of these processes, the resolution of the input data must allow the representation of the variability of precipitation as it represents a major source of variability in the model outputs. Currently, the use of stochastic weather generators is widespread to generate continuous series of meteorological data at gauged and ungauged locations. These weather simulators are designed to replicate the statistical properties of real weather data at monthly or daily time resolutions. However, daily values of precipitation do not represent the variability of storm parameters within a day, which is assumed to significantly influence the predictions of environmental or agricultural models where processes are sensitive to sub-daily values. This research proposes a parsimonious stochastic storm generator based on 5-min time resolution and correlated non-normal Monte Carlo-based numerical simulation. The model considers correlated non-normal random rainstorm characteristics such as time between storms, duration, and amount of precipitation, as well as the storm intensity structure. The accuracy of the model was verified by comparing the generated rainfall with rainfall data from a randomly selected 5-min weather station in North Carolina. Current results have shown that the proposed storm generator can capture the essential statistical features of rainstorms as well as their patterns followed by their intensities, preserving the first four moments of monthly storm events, good annual extreme event correspondence, and the correlation structure within each storm. Finally, as the proposed model depends on statistical properties at a site, this may allow the use of the synthetic storms in ungauged locations provided relevant information from a regional analysis is available. Keywords: Monte Carlo, Stochastic storm generator, Storm distribution.

The effect of phase change material incorporated building wall on the CO<SUB align="right">2 mitigation: a case study of Izmir, Turkey

Buildings are considered to be one of the considerable energy consuming systems and greenhouse gas emissions sources, and 14% of this energy is used for cooling. This paper evaluates the energetic benefits and CO2 emissions of incorporating phase change materials (PCMs) on the facade of an office building under real weather data conditions for Izmir, Turkey for cooling. For this purpose, two situations, specifically the effect of the melting temperature (27°C, 32°C and 37°C) and the location of PCM (interior, middle and exterior of the wall) have been investigated for five months, from May to September. The results indicate that integration of PCM with a melting temperature of 32°C at the interior section of the wall in the hottest months of the year, namely June, July, and August reduce the related CO2 emissions by 50.15, 122.339 and 121.727 kg CO2 respectively, compared to the baseline configuration without PCM.