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نتیجه جستجو - weather forecast

تعداد مقالات یافته شده: 15
ردیف عنوان نوع
1 Cultural consensus knowledge of rice farmers for climate risk management in the Philippines
دانش اجماع فرهنگی کشاورزان برنج برای مدیریت ریسک آب و هوایی در فیلیپین-2021
Despite efforts and investments to integrate weather and climate knowledges, often dichotomized into the scientific and the local, a top-down practice of science communication that tends to ignore cultural consensus knowledge still prevails. This paper presents an empirical application of cultural consensus analysis for climate risk management. It uses mixed methods such as focus groups, freelisting, pilesorting, and rapid ethnographic assessment to understand farmers’ knowledge of weather and climate conditions in Barangay Biga, Oriental Mindoro, Philippines. Multi-dimensional scaling and aggregate proximity matrix of items are generated to assess the similarity among the different locally perceived weather and climate conditions. Farmers’ knowledge is then qualitatively compared with the technical classification from the government’s weather bureau. There is cultural agreement among farmers that the weather and climate con- ditions can be generally grouped into wet, dry, and unpredictable weather (Maria Loka). Damaging hazards belong into two subgroups on the opposite ends of the wet and dry scale, that is, tropical cyclone is grouped together with La Ni˜na, rainy season, and flooding season, while farmers perceive no significant difference between El Ni˜no, drought, and dry spells. Ethnographic information reveals that compared to the technocrats’ reductive knowledge, farmers imagine weather and climate conditions (panahon) as an event or a phenomenon they are actively experiencing by observing bioindicators, making sense of the interactions between the sky and the landscape, and the agroecology of pest and diseases, while being subjected to agricultural regulations on irrigation, price volatility, and control of power on subsidies and technologies. This situated local knowledge is also being informed by forecasts and advisories from the weather bureau illustrating a hybrid of technical science, both from the technocrats and the farmers, and personal experiences amidst agricultural precarities. Speaking about the hybridity of knowledge rather than localizing the scientific obliges technocrats and scientists to productively engage with different ways of knowing and the tensions that mediate farmers’ knowledge as a societal experience.
keywords: دانش اجماع | پیش بینی آب و هوا | کشاورزی | خطر ابتلا به آب و هوا | Consensus knowledge | Weather forecasting | Agriculture | Climate risk
مقاله انگلیسی
2 Predicted direct solar radiation (ECMWF) for optimized operational strategies of linear focus parabolic-trough systems
تابش مستقیم خورشیدی پیش بینی شده (ECMWF) برای استراتژی های عملیاتی بهینه شده سیستم های سهموی-تمرکز خطی -2020
Day-ahead forecasts of direct normal irradiance (DNI) from the Integrated Forecasting System (IFS), the global model of the European Centre for Medium-Range Weather Forecasts (ECMWF), are used to simulate a concentrating solar power (CSP) plant through the System Advisor Model (SAM) to assess the potential value of the IFS in the electricity market. Although DNI forecasting from the IFS still demands advances towards cloud and aerosol representation, present results show substantial improvements with the new operational radiative scheme ecRad (cycle 43R3). A relative difference of approximately 0.12% for the total annual energy availability is found between forecasts and local measurements, while approximately 10.6% is obtained for the previous version. Results of electric energy injection to the grid from a simulated linear focus parabolic-trough system shows correlations coefficients of approximately 0.87 between hourly values of electric energy based on forecasted and measured DNI, while 0.92 are obtained for the daily values. In the context of control strategy, four operational strategies are given for different weather scenarios to handle the energy management of a CSP plant, including the effect of thermal energy storage capacity. Charge and discharge operational strategies are applied accordingly to the predicted energy availability.
Keywords: Short-term forecasts | ECMWF | Direct normal irradiance | Concentrating solar power | System advisor model | Operational strategies
مقاله انگلیسی
3 Influence of different time horizon-based battery energy management strategies on residential microgrid profitability
تأثیر استراتژیهای مختلف مدیریت انرژی باتری مبتنی بر افق زمان بر سودآوری میکروگریدهای مسکونی-2020
The growing share of renewable sources in future residential microgrids generates variability as well as price volatility on European electricity markets. Therefore, to handle this issue and enhance the system profitability, advanced energy management strategies should be developed. To that end, this paper proposes to assess the relevance of an energy management strategy based on 48-hour horizon compared to a 24-hour horizon one in order to perform energy arbitrage. This study considers a residential microgrid based on photovoltaic generation and storage connected to the main grid. Proposed 48-hour energy management strategy provides additional management possibilities such as the ability to delay trades (charge today, discharge tomorrow) and a larger range of hours to use the storage. Particle Swarm Optimizer is used to solve the optimization part. Besides, a sensitivity analysis is investigated to assess the economic impact of forced storage of solar surplus power in order to increase self-consumption rate and storage size. Obtained results demonstrates better profitability by using proposed strategy. Profitability was improved by more than 11% compared to classical algorithms for the tested scenarios. The findings of this study illustrate that the use of 48-hour horizon-based energy management strategy can be more profitable and lead residential microgrids to decrease their operation cost and increase power balance for all grid stakeholders through feed-in price leverage.
Keywords: Residential microgrid | Weather forecast uncertainties | Energy management strategies | Self-consumption | Grid services | Energy storage system
مقاله انگلیسی
4 Residential microgrid energy management considering flexibility services opportunities and forecast uncertainties
مدیریت انرژی ریز شبکه مسکونی با توجه به فرصتهای خدمات انعطاف پذیری و پیش بینی عدم قطعیت-2020
In the context of smart cities, the growing share of solar power induces uncertainty in power generation due to inherent climatic variations. Accurate forecasting will be a key point for future residential microgrids since inability to do so could dramatically impact power balance and grid stability. This paper proposes an enhanced energy management framework which aims to efficiently address uncertainty issues due to local climatic variations in a peninsula context. The proposed framework uses a ten-state Markov chain to generate stochastic solar irradiation as well as a forecast correction method based on recursive least-squares updated every hours in order to efficiently take part in hour-ahead power bidding process. Numerical results highlight benefits obtained by combining proposed forecast correction method and storage in a practical example of Saint-Nazaire, a city located in peninsula of Guérande, France. Besides, a sensitivity analysis regarding impact of storage size and aggregator penalty on operation cost and commitment indices is investigated. Obtained results demonstrates better accuracy in delivering power to the grid and will lead residential microgrids dealing with strong climatic variations to decrease their operation cost and increase power balance for all grid stakeholders.
Keywords: Residential microgrid | Uncertainties | Weather forecast | Markov chain | Recursive least squares | Grid services | Energy storage system
مقاله انگلیسی
5 Green house based on IoT and AI for societal benefit
خانه سبز مبتنی بر اینترنت اشیا و هوش مصنوعی برای منافع اجتماعی-2020
The paper “Greenhouse based on IoT and AI for societal benefit” using a native microcontroller (LPC2138), environment monitoring sensors, communication module (ESP8266), along with a server design (which takes into account the real time weather forecast, and data analysis on the data gathered by sensors for irrigation decision) is focused on achieving automation, IoT deployment level -3, wrong weather forecast counter-action in real time with automation and intelligent control for water utilisation and optimization which will result in a uniform yield. The system described optimizes water utilisation on the basis of plant’s water need instead of cultivator’s assumptions by working on static data such as plant and soil type and environment dynamic data gathered from sensors. The data has been tested for algorithms such as Naïve Bayes, C4.5 and SMO (svm). A web page has been created which can be used for monitoring the data of green house
Keywords: server | IoT | C4.5 | weather forecast | automation | irrigation | plant need
مقاله انگلیسی
6 Operation of a stationary hydrogen energy system using TiFe-based alloy tanks under various weather conditions
بهره برداری از سیستم انرژی هیدروژن ثابت با استفاده از مخازن آلیاژ مبتنی بر TiFe در شرایط مختلف آب و هوایی-2020
We describe the operation of a bench-scale stationary hydrogen energy system comprising photovoltaic (PV) panels, a water electrolyzer (Ely), metal hydride tanks fabricated using an AB-type TiFe-based alloy (TiFe-based tanks), fuel cells (FC), and batteries under various weather conditions. The FC and TiFe-based tanks are thermally coupled to transfer heat when necessary to stabilize the output power, and automatic control is provided via a building energy management system (BEMS), which plans the operating schedule up to 48 h in advance based on the weather forecast and expected demands of the building. Experiments were conducted for 24-h operation on a fine day, 48-h operations on partly cloudy and partly cloudy days, and 48-h operations on partly cloudy and rainy days in order to verify the system. Each operation was performed as planned. Our results show that it is possible to operate the hydrogen system all year round without external heat sources.
Keywords: Stationary hydrogen energy system | TiFe-Based alloy | Fuel cell | Building energy management | system | Thermal coupling
مقاله انگلیسی
7 Techniques Tanimoto correlated feature selection system and hybridization of clustering and boosting ensemble classification of remote sensed big data for weather forecasting
تکنیک های مربوط به سیستم انتخاب ویژگی Tanimoto و ترکیبی از خوشه بندی و افزایش طبقه بندی گروه از داده های بزرگ از راه دور برای پیش بینی آب و هوا-2020
Weather forecasting has been done using various techniques but still not efficient for handling the big remote sensed data since the data comprises the more features. Hence the techniques degrade the forecasting accuracy and take more prediction time. To enhance the prediction accuracy (PA) with minimal time, Tanimoto Correlation based Combinatorial MAP Expected Clustering and Linear Program Boosting Classification (TCCMECLPBC) Technique is proposed. At first, the data and features are gathered from big weather database. After that, relevant features are selected through finding the similarity between the features. Tanimoto Correlation Coefficient is used to find the similarity between the features for selecting the relevant features with higher feature selection accuracy. After selecting the relevant features, MAP expected clustering process is carried out to group the weather data for cluster formation. In this process, a number of cluster and cluster centroids are initialized. In this clustering process, it includes two steps namely expectation (E) and maximization (M) to discover maximum probability for grouping data into the cluster. After that, the clustering result is given to Linear Program boosting classifier to improve the prediction performance. In this classification, the weak classifier results are boosted to create strong classifier. The results evident that the TC-CMECLPBC technique enhance the PA with lesser time and false positive rate (FPR) than the conventional methods.
Keywords: Big data | Tanimoto correlation | MAP expected | Boosting classification | Expectation | Maximization | Similarity | Clustering | Cluster centroids | Strong classifier | Weak classifier
مقاله انگلیسی
8 A framework for distributed data mining heterogeneous classifier
چارچوبی برای طبقه بندی ناهمگن داده کاوی توزیع شده-2019
Distributed Data Mining (DDM) emerged as a huge area by the tremendous growth of geographically distributed data and powerful computational capability of computing. In this, ENcryption, NORMalization, MApping (ENORMA), a privacy preserving heterogeneous classifier framework for universal DDM is proposed. Three algorithms are proposed for maintaining data privacy, retrieval and integration on DDM. For data privacy, privacy-preserving algorithm is designed for protection of data in both the levels; for data retrieval, an algorithm is developed for value normalization and for integration, Mapping algorithm is developed to map the data with schema in global level. Experimental implementation on Electronic Health Records (EHRs), Job Recruitment Records (JRRs) and Agriculture Weather Forecast Records (AWFRs) datasets shows an improved result compared to conventional frameworks.
Keywords: Distributed data mining framework | Heterogeneous datasites | Privacy-preserving | Data normalization | Data integration
مقاله انگلیسی
9 Deep shared representation learning for weather elements forecasting
یادگیری نمایندگی اشتراکی عمیق برای پیش بینی عناصر آب و هوا-2019
The accuracy and reliability of weather forecasting are of importance for many economic, business and management activities. This paper introduces novel data-driven predictive models based on deep convolutional neural networks (CNN) architecture for temperature and wind speed prediction in weather data. In particular, the proposed deep learning framework employs different upgrading versions of the convolutional neural networks i.e. 1d-, 2d- and 3d-CNN. The introduced models exploit the spatio-temporal multivariate weather data for learning shared representations using historical data and forecasting weather elements for a number of user defined weather stations simultaneously in an end-to-end fashion. The embedded feature learning component of the models as well as coupling the learned features of different input layers have shown to have a significant impact on the prediction task. The proposed models show promising results compared to the classical neural networks architecture used for modeling nonlinear systems. Two experimental setups have been considered based on a dataset collected from the Weather Underground website at six stations located in Netherlands and Belgium as well as a larger dataset with higher temporal resolution from the National Climatic Data Center (NCDC) at five stations located in Denmark. First, we focus on simultaneously predicting the temperature of two main stations of Amsterdam and Brussels for 1–10 days ahead. The second experiment concerns wind speed prediction at three weather stations located in Denmark for 6 and 12 h ahead. The obtained numerical results show that learning new shared representations of the weather data by means of convolutional operations improves the prediction performance.
Keywords: Deep learning | Weather forecasting | Convolutional neural networks | Dimensionality reduction | Representation learning
مقاله انگلیسی
10 A dynamic neural network architecture with immunology inspired optimization for weather data forecasting
یک معماری شبکه عصبی پویا با ایمنولوژی بهینه سازی برای پیش بینی داده های آب و هوایی-2018
Recurrent neural networks are dynamical systems that provide for memory capabilities to recall past behaviour, which is necessary in the prediction of time series. In this paper, a novel neural network architecture inspired by the immune algorithm is presented and used in the forecasting of naturally occurring signals, including weather big data signals. Big Data Analysis is a major research frontier, which attracts extensive attention from academia, industry and government, particularly in the context of handling issues related to complex dynamics due to changing weather conditions. Recently, extensive deployment of IoT, sensors, and ambient intelligence systems led to an exponential growth of data in the climate domain. In this study, we concentrate on the analysis of big weather data by using the Dynamic Self Organized Neural Network Inspired by the Immune Algorithm. The learning strategy of the network focuses on the local properties of the signal using a self-organised hidden layer inspired by the immune algorithm, while the recurrent links of the network aim at recalling previously observed signal patterns. The proposed network exhibits improved performance when compared to the feedforward multilayer neural network and state-of-the-art recurrent networks, e.g., the Elman and the Jordan networks. Three non-linear and non-stationary weather signals are used in our experiments. Firstly, the signals are transformed into stationary, followed by 5-steps ahead prediction. Improvements in the prediction results are observed with respect to the mean value of the error (RMS) and the signal to noise ratio (SNR), however to the expense of additional computational complexity, due to presence of recurrent links.
Keywords: Recurrent Neural Networks ،Immune Systems Optimisation، Time Series Data analytics ، weather forecasting
مقاله انگلیسی
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