Solar energy assessment using remote sensing technologies

About 20% of the final energy consumed in Europe is used in buildings. The active and passive use of solar energy is an approach to reduce the fossil energy consumption and the greenhouse gas emissions originated by buildings. Consideration of solar energy technologies in urban planning demands accurate information of the available solar resources. This can be achieved by the use of remote sensing data from geostationary satellites which show a very high spatial and a sufficient temporal resolution compared to ground station data. This paper gives a brief introduction to the HELIOSAT method applied to derive surface solar irradiance from satellite images and shows examples of applications: The use of daylight in buildings, the generation of correlated time series of solar irradiance and temperature as input data for simulations of solar energy systems and a short-term forecast of solar irradiance which can be used in intelligent building control techniques. Finally an outlook is given on potential improvements expected from the next generation of European meteorological satellites Meteosat Second Generation (MSG).     

Control allocation-based adaptive control for greenhouse climate

This paper presents an adaptive approach to greenhouse climate control, as part of an integrated control and management system for greenhouse production. In this approach, an adaptive control algorithm is first derived to guarantee the asymptotic convergence of the closed system with uncertainty, then using that control algorithm, a controller is designed to satisfy the demands for heat and mass fluxes to maintain inside temperature, humidity and CO₂ concentration at their desired values. Instead of applying the original adaptive control inputs directly, second, a control allocation technique is applied to distribute the demands of the heat and mass fluxes to the actuators by minimising tracking errors and energy consumption. To find an energy-saving solution, both single-objective optimisation (SOO) and multiobjective optimisation (MOO) in the control allocation structure are considered. The advantage of the proposed approach is that it does not require any a priori knowledge of the uncertainty bounds, and the simulation results illustrate the effectiveness of the proposed control scheme. It also indicates that MOO saves more energy in the control process.     

Satellite Application Facilities irradiance products: hourly time step comparison and validation over Europe

Downward short- and longwave incoming irradiances play a key role in the radiation budget at the Earth's surface. Monitoring these parameters is essential for understanding the basic mechanisms involved in climate change, such as the greenhouse effect, global dimming, and changes in cloud cover and precipitation. Geostationary satellite observations are important in the retrieval of irradiance at the surface, providing excellent spatial and temporal coverage. Three decentralized Satellite Application Facilities (SAFs) are currently operational in the European Organisation for the Exploitation of Meteorological Satellites (Eumetsat), involved in retrieving surface solar irradiance (SSI) and downward longwave irradiance (DLI) from Meteosat images. This study presents a common validation of these radiation products against ground data from eight stations covering four months representative of the annual declination variation. The overall conclusion is that the products of the different SAFs are comparable in terms of bias and standard deviation. The SSI is retrieved with a standard deviation of 80–100 W m^?2 and negligible bias, and the DLI with a standard deviation of 25 W m^?2 with a slight site-dependent bias.     

ECO–ALOC: Energy-efficient resource allocation for cluster-based software routers

In this paper, we explore the use of cluster-based software routers as a way to share hardware resources, reduce costs, and save energy. We propose ECO–ALOC, an energy-efficient resource allocation mechanism that reconfigures the router according to the traffic demands using two modules. The first module provides fine-grained energy consumption control by switching CPU operation frequencies among three special frequencies. The second module provides long-term power savings by using virtual router migration to consolidate the load and shut idle servers down. We simulate a cluster-based software router using real traffic traces to evaluate our proposal. Results show that ECO–ALOC provides power savings of up to 93% depending on the cluster load scenario.     

Coordination control of greenhouse environmental factors

Optimal control of greenhouse climate is one of the key techniques in digital agriculture. Greenhouse climate, a nonlinear and uncertain system, consists of several major environmental factors such as temperature, humidity, light intensity, and CO₂ concentration. Due to the complex coupled correlations, it is a challenge to achieve coordination control of greenhouse environmental factors. This paper proposes a model-free coordination control approach for greenhouse environmental factors based on Q-learning. Coordination control policy is found through systematic interaction with the dynamic environment to achieve optimal control for greenhouse climate with the control cost constraints. In order to decrease systematic trial-and-error risk and reduce the computational complexity in Q-learning algorithm, case-based reasoning (CBR) is seamlessly incorporated into the Q-learning process. The experimental results demonstrate that this approach is practical, highly effective and efficient.     

Ozone monitoring in Salekhard and Tomsk,western Siberia

Atmospheric ozone plays an important role in understanding the processes occurring in the atmosphere and changes in the climate. This article relates to experimental results from balloon-borne research in the stratosphere for the study of chemical and dynamical processes influencing climate change and for validation of satellite observations. Total ozone observations in western Siberia were performed by the Brewer MKIV S/N 049 spectrophotometer in Tomsk and the SAOZ UV–Vis spectrometer in Salekhard. We also use 2Z-ECC ozonesondes for ozone profile observations in the winter/spring period at the Salekhard aerological station. During the winter/spring season of 2011, Arctic ozone in the 19–21 km altitude region was observed to be more than 70% less than typical values. In the winter/spring season of 2012, on the other hand, Arctic conditions were overall much warmer than in 2011, and no evidence of significant ozone loss was seen above the Asiatic regions of the Russian Federation. The aim of the article is to describe which and where these measurements were carried out and illustrate their performances by some examples of ozone data measured in western Siberia, Russia, such as that which occurred in the winter/spring season of 2011.     

Nonlinear MPC based on a Volterra series model for greenhouse temperature control using natural ventilation

Suitable environmental conditions are a fundamental issue in greenhouse crop growth and can be achieved by advanced climate control strategies. In different climatic zones, natural ventilation is used to regulate both the greenhouse temperature and humidity. In mild climates, the greatest problem faced by far in greenhouse climate control is cooling, which, for dynamical reasons, leads to natural ventilation as a standard tool. This work addresses the design of a nonlinear model predictive control (NMPC) strategy for greenhouse temperature control using natural ventilation. The NMPC strategy is based on a second-order Volterra series model identified from experimental input/output data of a greenhouse. These models, representing the simple and logical extension of convolution models, can be used to approximate the nonlinear dynamic effect of the ventilation and other environmental conditions on the greenhouse temperature. The developed NMPC is applied to a greenhouse and the control performance of the proposed strategy will be illustrated by means of experimental results.     

Integrated assessment model of society-biosphere-climate-economy-energy system

The feedback based integrated assessment model ANEMI_2 represents the society-biosphere-climate-economy-energy system of the earth and biosphere. The ANEMI_2 model is based on the system dynamics simulation approach that (a) allows for the understanding and modeling of complex global change and (b) assists in the investigation of possible policy options for mitigating, and/or adapting to changing global conditions within an integrated assessment modeling framework. This paper outlines the ANEMI_2 model and its nine system components: climate, carbon cycle, land-use, population, food production, hydrologic cycle, water demand, water quality, and energy-economy. To evaluate market and nonmarket costs and benefits of climate change, the ANEMI_2 model integrates an economic optimization approach, with a focus on the international energy stock and fuel price, climate interrelations and temperature change. The model takes into account all major greenhouse gases (GHG) influencing global temperature and sea-level variation. Results from several scenarios (a) compare well with other information available in the scientific literature, (b) present comprehensive response of the society-biosphere-climate-economy-energy system to the selected scenarios, and (c) confirm the support role of the ANEMI_2 model in the policy development and analyses.     

Rule-based approaches for energy savings in an ambient intelligence environment

This paper presents a novel real-world application for energy savings in a Smart Building environment. The proposed system unifies heterogeneous wireless sensor networks under a Semantic Web Service middleware. Two complementary and mutually exclusive rule-based approaches for enforcing energy-saving policies are proposed: a reactive agent based on production rules and a deliberative agent based on defeasible logic. The system was deployed at a Greek University, showing promising experimental results (at least 4% daily savings). Although the percentage of energy savings may seem low, the greatest merit of the method is ensuring that no energy is wasted by constantly enforcing the policies.     

Analysis of the carbon dioxide concentration in the lowest atmospheric layers and the factors affecting China based on satellite observations

Carbon dioxide (CO₂) is the most important anthropogenic greenhouse gas contributing to global climate change. SCIAMACHY on board ENVISAT (launched in 2002) is the first satellite instrument to monitor the changes in CO₂ concentration in the lowest atmospheric layers. The temporal and spatial distribution of CO₂ (2003–2009) concentration based on SCIAMACHY over China is presented and discussed. It shows an annual increase and a seasonal cycle. The CO₂ annual growth rate was about 1.8 ppm year^?1, with the highest value being in spring and the lowest in autumn. The CO₂ concentration variation is determined by many complex factors. In this article, we analyse the important factors affecting CO₂ variations, with special emphasis on terrestrial ecosystems and energy consumption. Terrestrial ecosystems are an important sink in the global carbon cycle. The relationship between CO₂ concentration and Moderate Resolution Imaging Spectroradiometer (MODIS) net primary production (NPP) in 2008 is analysed. CO₂ concentration is inversely proportional to NPP both in regions with high-density vegetation and in deserts. The Yunnan province has the highest NPP value and the lowest CO₂ concentration, whereas the Takla Makan Desert has the lowest NPP value and the highest CO₂ concentration. Energy consumption is the main emission source of atmospheric CO₂. CO₂ emissions from energy consumption show a steady increase in China since 1980. China's CO₂ concentration variation shows a high correlation with energy consumption (coefficient of determination (R ²) > 0.8). The regions with high energy consumption are major industrial regions such as Shandong, Guangdong, Jiangsu, Zhejiang, Hebei, and Henan.     

无线传感器网络在温室环境监测中的应用研究

以温室环境监测为应用背景,分析温室环境监控的特点,给出温室环境监控的系统体系结构,并结合温室监控的具体应用提出节能的数据融合算法,仿真实验表明该算法能有效提高节点能量的利用率和延长网络生命周期。     

基于单片机的智能化节能控制系统设计

本文分析了当前一些公共室内场所存在的用电浪费情况,设计了基于单片机的智能化节能控制系统。该系统在 自动控制模式下,能根据场所的光线亮度,检测有无人的情况和实时温度,自动开启或关闭照明灯和电风扇,从而实现 对灯管、风扇用电的智能化自动控制,达到节约用电的目的。     

渐变刚度板簧式轻卡悬架阻尼参数仿真设计

为了有效提高渐变刚度钢板弹簧式轻型卡车的乘坐舒适性,根据弹簧受力与刚度和变形之间的关系,利用等频原理,建立了渐变刚度钢板弹簧力学特性解析计算模型,并利用钢板弹簧加载-卸载试验对所建立的模型的正确性进行了验证。基于1/2车辆行驶振动模型,利用MATLAB的Simulink工具箱,建立了考虑渐变刚度钢板弹簧力学特性的轻卡行驶振动仿真分析模型。最后,以人体振动舒适性最佳为目标,利用多岛遗传算法,建立了轻卡悬架系统阻尼参数仿真设计方法,并利用实车试验对所建立的轻卡悬架系统阻尼参数仿真设计方法的正确性进行了验证。上述研究为渐变刚度钢板弹簧式轻卡悬架系统参数的设计提供了有效参考。     

A long short-term memory-based model for greenhouse climate prediction

Greenhouses can grow many off-season vegetables and fruits, which improves people's quality of life. Greenhouses can also help crops resist natural disasters and ensure the stable growth of crops. However, it is highly challenging to carefully control the greenhouse climate. Therefore, the proposal of a greenhouse climate prediction model provides a way to solve this challenge. We focus on the six climatic factors that affect crops growth, including temperature, humidity, illumination, carbon dioxide concentration, soil temperature and soil humidity, and propose a GCP_lstm model for greenhouse climate prediction. The climate change in greenhouse is nonlinear, so we use long short-term memory (LSTM) model to capture the dependence between historical climate data. Moreover, the short-term climate has a greater impact on the future trend of greenhouse climate change. Therefore, we added a 5-min time sliding window through the analysis experiment. In addition, sensors sometimes collect wrong climate data. Based on the existence of abnormal data, our model still has good robustness. We experienced our method on the data sets of three vegetables: tomato, cucumber and pepper. The comparison shows that our method is better than other comparison models.     

Inferences of all‐sky solar irradiance using Terra and Aqua MODIS satellite data

Solar irradiance is a key environmental control, and accurate spatial and temporal solar irradiance data are important for a wide range of applications related to energy and carbon cycling, weather prediction, and climate change. This study presents a satellite‐based scheme for the retrieval of all‐sky solar irradiance components, which links a physically based clear‐sky model with a neural network version of a rigorous radiative transfer model. The scheme exploits the improved cloud characterization and retrieval capabilities of the MODerate resolution Imaging Spectroradiometer (MODIS) onboard the Terra and Aqua satellites, and employs a cloud motion tracking scheme for the production of hourly solar irradiance data throughout the day. The scheme was implemented for the Island of Zealand, Denmark (56° N, 12° E) and Southern Arizona, USA (31° N, 110° W) permitting model evaluation for two highly contrasting climates and cloud environments. Information on the atmospheric state was provided by MODIS data products and verifications against AErosol RObotic NETwork (AERONET) data demonstrated usefulness of MODIS aerosol optical depth and total precipitable water vapour retrievals for the delineation of spatial gradients. However, aerosol retrievals were significantly biased for the semi‐arid region, and water‐vapour retrievals were characterized by systematic deviations from the measurements. Hourly global solar irradiance data were retrieved with overall root mean square deviations of 11.5% (60 W m^?2) and 26.6% (72 W m^?2) for Southern Arizona and the Island of Zealand, respectively. For both regions, hourly satellite estimates were shown to be more reliable than pyranometer measurements from ground stations only 15 km away from the point of interest, which is comparable to the accuracy level obtainable from geostationary satellites with image acquisitions every 15–30 min. The proposed scheme is particularly useful for solar irradiance mapping in high‐latitude regions as data from geostationary satellites experience a gradual degradation in spatial resolution and overall quality with latitude and become unusable above approximately 60° latitude. However, in principle, the scheme can be applied anywhere on the globe, and a synergistic use of MODIS and geostationary satellite datasets may be envisaged for some applications.     

On the Earth's surface energy exchange determination from ERS satellite ATSR data: Part 2. Short-wave radiation

This is the second in a series of papers which discusses determination of the Earth's surface energy exchange from ERS satellite Along-Track Scanning Radiometer (ATSR) data. The paper concentrates on short-wave radiation on sea and land surfaces. In this paper, three methods were used to determine solar irradiance by using ERS ATSR-2 data. We referred to them as 'D scheme', 'T scheme' and 'O scheme'. Intercomparisons of the three schemes were carried out. The schemes were applied to the land and sea areas. The visible and near-infrared reflectances were derived from ERS-2 ATSR-2 spectral bands by using the atmospheric radiative transfer model developed by Xue and Cracknell. The narrowband reflectances are combined into a measure of surface albedo by use of a weighted averaged scheme. The schemes were applied to the land and sea areas in UK and deforestation area in Brazil. The D scheme can give solar spectral irradiance but can not give broadband solar irradiance because of the wavelength limit of sensor visible bands. The T scheme and O scheme can give good broadband solar irradiance but can not give solar spectral irradiance. The O scheme was developed by Oberhuber, which was used to create climatological datasets for GCMs. The O scheme also includes the effects of humidity and surface temperature. The O scheme is better used for daily or monthly averaged solar radiation. The other two schemes can also be developed to determine the hourly or daily solar irradiance. The results show that it is now possible to derive longterm surface solar irradiance from ATSR-2 data which can be useful in climate and hydrological studies. However, our current analysis is restricted to a small range of conditions and needs to be extended to a larger dataset.     

The significance of crop co-states for receding horizon optimal control of greenhouse climate

While a tomato crop grows on the time-scale of weeks, the greenhouse climate changes on a time-scale of minutes. The economic optimal control problem of producing good quality crops against minimum input of resources is tackled by a two time-scale decomposition. First, the sub-problem associated to the slow crop evolution is solved off-line, leading to a seasonal pattern for the co-states of the amount of assimilates produced by photosynthesis, and the fruit and leaf weights. These co-states can be interpreted as the marginal prices of a unit of assimilate, leaf and fruit. Next, they are used in the goal function of an on-line receding horizon control (RHOC) of the greenhouse climate, thus balancing costs of heating and CO₂-dosage against predicted benefits from harvesting, while profiting as much as possible from the available solar radiation. Simulations using the time-varying co-states are compared to experimental results obtained with fixed co-states. It appears that the on-line control is sensitive to the time evolution of the co-states, suggesting that it is advantageous to repeat the seasonal optimisation from time to time to adjust the co-states to the past weather and realised crop state.     

Optimal configuration of Energy-Efficient Ethernet

The IEEE 802.3az standard provides a new low power mode that Ethernet network interfaces can use to save energy when there is no traffic to transmit. Simultaneously with the final standard approval, several algorithms were proposed to govern the physical interface state transition between the normal active mode and the new low power mode. In fact, the standard leaves this sleeping algorithm unspecified to spur competition among different vendors and achieve the greatest energy savings. In this paper, we try to bring some light to the most well known sleeping algorithms, providing mathematical models for the expected energy savings and the average packet delay inflicted on outgoing traffic. We will then use the models to derive optimum configuration parameters for them under given efficiency constraints.     

NSGA-II-based nonlinear PID controller tuning of greenhouse climate for reducing costs and improving performances

The traditional tuning scheme of proportional, integral, and derivative (PID) controller parameters usually lay more emphasis on control performances than economic profits. As a result, the corresponding control performance is improved, but such case may lead to high production costs. In this paper, a new tuning methodology for multiple PID controllers from an economic point of view by incorporating multiple performance measures and production costs based on nondominated sorting genetic algorithm-II (NSGA-II) is presented. A model of nonlinear thermodynamic laws between numerous system variables affecting the greenhouse climate is formulated. The proposed tuning scheme is tested through step responses for greenhouse climate control by minimizing the indices of overall performance and production cost in a simulation experiment. The results show that the controllers by tuning the gain parameters can achieve good control performance at a relatively low cost. Maybe it is a quite effective and promising tuning method by using this method in the complex greenhouse production.