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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 |