Actual Weather Data Research Articles

Optimal power management of a stand-alone hybrid energy management system: Hydro-photovoltaic-fuel cell

The field of electrical engineering with renewable energy systems has witnessed a remarkable improvement due to an increase in the world's growing population as available resources become limited. This study proposes an optimal and efficient hybrid energy management system that combines photovoltaic, hydro, and fuel cell renewable energy resources that can solve the drawbacks of current energy setups and offer a dependable power source for stand-alone purposes. This novel AC-linked hybrid energy system features a comprehensive power management strategy with actual weather data and a realistic load demand profile. The HOMER Pro technique is employed for calculating the emission of toxic gases, system economic benefits, sensitivity analysis and cost minimization of the system while providing sustainability. The numerical outcomes show optimal results as compared to the existing systems in the literature. The system consists of 55 kW of PV modules, a 30 kW FC generator, and 40 kW of hydropower, suggesting optimal and excellent economic and environmental sustainability. This system appeared to be an especially cost-effective solution, with a net present cost (NPC) of $248,773 and a cost of energy (COE) of $0.0546/kWh. The results show that the system can meet energy needs while utilising the available resources efficiently in different weather conditions. The proposed system has the potential to increase the sustainability and dependability of power generation in a variety of applications and aid in the design and implementation of alternative energy systems, particularly in off-grid or remote areas. The proposed system can be extended to other such locations that will provide a scalable and flexible solution for electricity generation, making it suitable for a variety of applications, from small rural power plants to large industrial power supplies. Overall, the proposed study provides an important resource for development in hybrid energy system design and operation, with far-reaching implications for the transition to a sustainable and low-carbon future.

Microalgae bio-reactive façade: A model coupling weather, illumination, temperature, and cell growth over the year

This article presents the development of a numerical model coupling the thermal and biological behaviors of a microalgae biofaçade. Heat fluxes and illumination are modulated by actual weather data, and the system response is evaluated in terms of biomass and pigment production. First, the effect of the different model refinements are screened. Then, the model is applied to an illustrative case: the city of Marseille over the year 2023. The results illustrate the model’s capabilities, such as assessing the building/biofaçade thermal synergy or the summer/winter operational differences. The numerical behavior of the model is also analyzed: local and global sensitivity analyses assess the impact of uncertain parameters. The parameter inducing the highest amount of uncertainty is the microalgae photoconversion efficiency, highlighting the need to ascertain its value before designing a system. Despite this uncertainty, even under conservative assumptions, the predictions of the model are 10 % accurate.

Microalgae bio-reactive façade: System thermal–biological optimization

This article explores numerically the biotechnological performances of microalgae biofaçade. The model computes the system’s thermal behavior using a radiative-convective approach accounting for location on Earth and actual weather data. In a coupled manner, it simulates the microalgae culture behavior, i.e. light-driven growth and cell pigment content acclimation. In addition, it features refinement such as wavelength-dependent biomass optical properties and thermal-modulated biological rates. Thanks to this model, operation strategies and design possibilities were evaluated using actual weather data for a biofaçade module deployed in Marseille in 2023. Investigations revealed that a semi-batch mode of operation, while simplistic, is the most efficient way to operate a biofaçade if sole biological production is considered (about 18.0 ± 0.9 kg per year, 2.44 ± 0.12 g/L output concentration). However, if intended as an office glazing, turbidostat mode of operation should be preferred for aesthetic and visual comfort reasons (about 19.1 ± 1.1 kg per year, 0.64 ± 0.07 g/L output concentration). System optimization also confirmed the experimental observation that the system could be prone to overheating. Nevertheless, while overheating can be mitigated by increasing the reservoir thickness, this strategy is detrimental to the average output concentration. Finally, location-specific optimization revealed that a standard biofaçade module could be deployed over France, and system performances are derived for the whole country thanks to the weather forecast agency data.

Comparative analysis of the dispersion modeling and dose projection results performed under BARCO international project

Radiological assessments on zones to take protective actions in case of a nuclear or radiological emergency involve a series of real-time forecasts of radiological impact on the public at various distances from the release point, using actual weather or forecast data, information on the source term or facility status, and primary radiation monitoring data. This practice is implemented during the operation of emergency centers around the world in order to promptly report the occurrence and possible consequences of radiological accidents in the country and abroad in the event of a possible transboundary impact. Since the Chornobyl disaster, a lot of emergency exercises, research programs and projects, in particular, benchmarking, have served as international platforms for improving modeling capacity in atmospheric dispersion. This activity is carried out both on the basis of past severe accidents with significant atmospheric releases and corresponding radiological consequences, and on the basis of specific conditional (hypothetical) events that are developed in accordance with the purpose of the study. The paper is focused on the comparison results performed under the international project “Benchmarking on Assessment of Radiological COnsequences” (BARCO) conducted in 2020–2021 between five technical support organisations – members of the European Technical Safety Organisations Network (ETSON). The work contains a short overview of relevant international activity conducted in the past, a description of the BARCO project and its objectives, a list of participants, project tasks, initial data (source term, meteorology, list of benchmarking quantities, approach to data exchange, codes used). The study presents some of comparative analysis results obtained via two techniques such as code-to-code analysis (CTCA) and matched-pair analysis (MPA). The results discussion concentrates on the overall recommendations for code users. Conclusions provide the main outputs of the project.

Using Passive Multi-Modal Sensor Data for Thermal Simulation of Urban Surfaces

Abstract. This paper showcases an integrated workflow hinged on passive airborne multi-modal sensor data for the simulation of the thermal behavior of built-up areas with a focus on urban heat islands. The geometry of the underlying parametrized model, or digital twin, is derived from high-resolution nadir and oblique RGB, near-infrared and thermal infrared imagery. The captured bitmaps get photogrammetrically processed into comprehensive surface models, terrain, dense 3D point clouds and true-ortho mosaics. Building geometries are reconstructed from the projected point sets with procedures presupposing outlining, analysis of roof and fac¸ade details, triangulation, and texturing mapping. For thermal simulation, the composition of the ground is determined using supervised machine learning based on a modified multi-modal DeepLab v3+ architecture. Vegetation is retrieved as individual trees and larger tree regions to be added to the meshed terrain. Building materials are assigned from the available visual, infrared and surface planarity information as well as publicly available references. With actual weather data, surface temperatures can be calculated for any period of time by evaluating conductive, convective, radiative and emissive energy fluxes for triangular layers congruent to the faces of the modeled scene. Results on a sample dataset of the Moabit district in Berlin, Germany, showed the ability of the simulator to output surface temperatures of relatively large datasets efficiently. Compared to the thermal infrared images, several insufficiencies in terms of data and model caused occasional deviations between measured and simulated temperatures. For some of these shortcomings, improvement suggestions within future work are presented.

Regression Learner Application Model-Based Short-Term Load Forecasting for Mascouche (Quebec, Canada)

Load forecasting is crucial for power systems optimal operation and allows power utilities to overcome technical and economic issues. Some forecasting techniques are currently being deployed on a large scale to meet the requirements of increased energy demand while balancing it with the production to achieve socio-economic benefits for sustainable development. In this paper, we are diving into the forecasting using the regression method. We are focusing on short-term load forecasting and how it can give businesses valuable insights into future sales, labor needs, and more. Power utilities use short-term load forecasting technology to make reasonable power systems. A forecasting model with low prediction errors helps reduce operating costs and risks for the operators leading to models’ optimization. To make things real, we are using actual load and weather data from the Hydro-Quebec database. We will be exploring the capabilities, advantages, and limitations of this method, all while keeping an eye on the changing landscape of electricity supply and demand. Our study is centered around the Mascouche region in Quebec, Canada, where the load fluctuates between 60 to 140 megawatts.

Microalgae bio-reactive façade: A radiative-convective model powered by hourly illumination computation and historical weather data

This article presents a numerical model predicting the performance of a microalgae biofaçade. The core of the model is the association of radiative, convective, and conductive heat transfers. These key physical phenomena are modulated by actual weather data and coupled to a biological model of the microalga Chlorella vulgaris. Based on the conjunction of temperature and illumination predictions, the biofaçade performances are categorized between low, adequate, high temperature × light-deficient, light-sufficient conditions. The capabilities of the model (dissection of acute events to year-round performance prediction) are illustrated using the city of Marseille, France, over the year 2020. The numerical behavior of the model itself is then analyzed. The influence of the Urban Heat Island submodel is discussed, and a global sensitivity analysis (Sobol’s indices) is led to assess the impact of uncertain physical parameters. The model accuracy is evaluated at 7.4% of the total daytime (under conservative assumptions) . The most influential parameters are the microalgae culture emissivity and the building indoor emissivity. All in all, the confidence in this model is high enough so that it can be used to design a biofaçade numerically.

Experience with extreme weather events increases willingness-to-pay for climate mitigation policy

We explore how extreme event experience relates to climate policy support in the U.S. We add three important yet uncommon elements to this field: we verify self-reports of extreme event experience with actual weather data; we use a willingness-to-pay measure to assess behavioral intention; and we analyze which types of extreme events have stronger impacts on WTP. People who self-report extreme weather events are willing to pay approximately $112/year more for climate mitigation policy than those who do not; people for whom those self-reports match recorded data are willing to pay $106 or $71 more (controlling for climate beliefs and political ideology and depending on how unverified reporters are treated). Wildfires have the strongest influence on WTP. Though our results show that political ideology correlates more strongly with policy support than does extreme event experience, extreme event experience exhibits a robust correlation with policy support, and could result in a minimum of billions of dollars of support annually for clean-energy policy alone.

Improving Accuracy of Daily Weather Forecast Model at Soekarno-Hatta Airport Using BILSTM with SMOTE and ADASYN

Bidirectional LSTM (BiLSTM) is an extension of LSTM which can improve model efficiency and accuracy in classification scenarios based on time series data or longer time series data repeatedly. This research uses the BiLSTM algorithm to build a daily weather forecast model at Soekarno-Hatta Airport. The model built will assist forecasters in making weather forecasts on a local scale. This research is expected to be implemented and able to increase the verification value of Soekarno-Hatta Airport weather forecasts to support flight safety in Indonesia. The dataset used is hourly surface air weather parameter data (synoptic data) of Soekarno-Hatta Meteorological Station for the period January 2018 - December 2022. There is an imbalance in the data set, so the SMOTE and ADASYN techniques are used to handle the problem. The output of this research is weather conditions categorised into sunny, sunny cloudy, cloudy, light rain, moderate rain, heavy rain, and thunder rain. The results obtained will go through model verification and evaluation by finding the accuracy value by comparing the weather forecast model output with actual weather data using a multi-category contingency table. The BiLSTM - ADASYN model obtained the highest average accuracy value compared to other models, which was 83.2%.

Predictive Modeling of Photovoltaic Panel Power Production through On-Site Environmental and Electrical Measurements Using Artificial Neural Networks

Weather disturbances pose a significant challenge when estimating the energy production of photovoltaic panel systems. Energy production and forecasting models have recently been used to improve energy estimations and maintenance tasks. However, these models often rely on environmental measurements from meteorological units far from the photovoltaic systems. To enhance the accuracy of the developed model, a measurement Internet of Things (IoT) prototype was developed in this study, which collects on-site voltage and current measurements from the panel, as well as the environmental factors of lighting, temperature, and humidity in the system’s proximity. The measurements were then subjected to correlation analysis, and various artificial neural networks (ANNs) were implemented to develop energy estimations and forecasting models. The most effective model utilizes lighting, temperature, and humidity. The model achieves a root mean squared error (RMSE) of 0.255326464. The ANN models are compared to an MLR model using the same data. Using previous power measurements and actual weather data, a non-autoregressive neural network (Non-AR-NN) model forecasts future output power values. The best Non-AR-NN model produces an RMSE of 0.1160, resulting in accurate predictions based on the IoT device.

Thermo-economic assessment and systematic comparison of combined supercritical CO2 and organic Rankine cycle (SCO2-ORC) systems for solar power tower plants

Solar power towers (SPTs) integrated with thermal energy storage are promising solutions for solar energy utilisation. Supercritical CO2 power cycles are acknowledged as an attractive option for dry-cooling SPT plants. However, abundant low-grade waste heat exists in the SCO2 gas cooler, which is directly dissipated to the ambient. In order to further improve the performance of SCO2-based SPT plants, organic Rankine cycle (ORC) systems can be introduced as a bottoming cycle subsystem. In this paper, an ORC subsystem is added to four different SCO2 cycle layouts, i.e., recuperated (RE), recompression (RC), intercooling (IC), and partial cooling (PC) cycles, to form combined cycle systems for SPT applications. A parametric study reveals that the thermal efficiency of the power block (i.e., the whole power cycle system), and the salt temperature difference across the receiver are the determining factors of the thermo-economic performance of SPT-SCO2-ORC plants. Design optimisation and annual performance evaluations are implemented using actual weather data in Delingha, China. Compared to the SPT plant with a standalone recuperated SCO2 cycle system, the annual electricity generation is increased by 19 % by integrating a bottoming ORC subsystem. The SPT-RC-ORC system produces the maximum electricity generation of 123 GW·h/year, while the SPT-RE-ORC system achieves the lowest levelised cost of electricity (LCOE) of 0.12 $/kW·h and the minimum payback time of 8.8 years. A wide range of solar irradiance conditions is further considered to generalise the performance evaluation of such combined cycle systems, with results showing that with the highest solar irradiance investigated (3700 sunshine hours, 1000 W/m2·h), the LCOE of the SPT-RE-ORC plant can be as low as 0.07 $/kW·h and the payback time is as short as 6 years. This study investigates optimal SCO2-ORC configurations for SPT applications based on annual performance evaluations, and presents preliminary assessments of the thermo-economic potential of ushc SPT-SCO2-ORC plants across a variety of solar resource conditions.

Multi-forward-step battery voltage prediction for real-world electric vehicles using gated recurrent units

Accurate battery voltage prediction performs a critical role in electric vehicles, especially safety risks prognosis in future periods. This paper proposes a multi-forward-step battery voltage prediction method using gated recurrent units. Actual vehicle operation data and weather data are used together for model training. A two-step procedure including data cleaning and correlation analysis is conducted to improve training efficiency and prediction accuracy. Furthermore, the effects of different model parameters on the prediction results are also investigated to obtain an optimal set of model parameters. Additionally, the model is tested with the data of the second year. The results proved that the model not only has a high prediction accuracy, but also has a strong stability and robustness. On this basis, a research plan for real-time multi-forward-step battery voltage prediction is further developed, whereby identifying occurrence of battery failure and mitigate the risk of thermal runaway by voltage predicting.

Solar Radiation and Weather Analysis of Meteorological Satellite Data by Tensor Decomposition

In this study, the data obtained from meteorological satellites were analyzed using tensor decomposition. The data used in this paper are meteorological image data observed by the Himawari-8 satellite and solar radiation data generated from Himawari Standard Data. First, we applied Higher-Order Singular Value Decomposition (HOSVD), a type of tensor decomposition, to the original image data and analyzed the features of the data, called the core tensor, obtained from the decomposition. As a result, it was found that the maximum value of the core tensor element is related to the cloud cover in the observed area. We then applied Multidimensional Principal Component Analysis (MPCA), an extension of principal component analysis computed using HOSVD, to the solar radiation data and analyzed the Principal Components (PC) obtained from MPCA. We also found that the PC with the highest contribution rate is related to the solar radiation in the entire observation area. The resulting PC score was compared to actual weather data. From the result, it was confirmed that the temporal transition of the amount of solar radiation in this area can be expressed almost correctly by using the PC score.

Reversible Solid Oxide Cells for Energy Storage in the Netherlands: Thermodynamics and Fuel Selection

As a famously flat and low-lying country, the Netherlands has no sites for hydro-power at a large scale, and high-temperature geothermal energy is not easily accessible. Therefore, the Netherlands will need to depend on intermittent renewables such as wind and solar energy for decarbonizing its energy system. This results in a definite need for large scale energy storage in the future Dutch energy landscape. With the lack of potential for pumped hydro storage, but easy access to large salt caverns and gas fields, energy storage in e-fuels (i.e. power-to-X-to-power) seems to be the most likely candidate for energy storage systems in the Netherlands at such a large scale. Among the various technologies available to implement such systems, reversible solid oxide cells (rSOC) are very suitable due to high efficiency and fuel flexibility, in addition to higher capacity utilization made possible by reversible operation.In this study, we investigated three fuels — hydrogen, methane and ammonia — to find the most suitable fuel for use in a standalone rSOC-based energy storage system to balance a future Dutch electrical grid powered entirely by intermittent renewables. In particular, the fuels were compared in terms of the storage capacity and the renewable power capacity that would be required if each of these fuels were used. The effect of a limited use of biomass to reduce energy storage needs was also investigated. Energy from biomass was assumed to stay around the same level as it is at present. Heat from nuclear, concentrated solar, or industrial sources was not considered.Process designs of rSOC systems using each fuel were modelled in Aspen Plus, and the operating points were optimized for round trip efficiency using exergy analysis. The power-fuel flow relationships derived from these models were then used in a model of an energy grid implemented in the programming language R. This model used actual hourly weather data and Dutch energy demand over a period of five years (2014–2018) for supply/demand calculations with a temporal resolution of one day. The required storage capacity and power generation capacity were derived from the results of this energy grid model. Supply-demand imbalances within each day-long interval were assumed to be balanced using batteries, and was outside the scope of this study.From the results of this study, it appears that for reversible SOC systems under the set of assumptions taken, a hydrogen-based system can be more efficient than systems using methane or ammonia. This implies that a grid using hydrogen as its energy storage medium can meet the same demand with a smaller power generation capacity compared to the other fuels. We also found the methane system to be slightly more efficient than the ammonia system.On the other hand, the comparison of storage capacities revealed that the methane system has significantly lower gas storage requirement than the hydrogen system for the same energy demand. The ammonia system also requires larger storage capacity for gases than the methane system, in addition to tanks for liquid ammonia. The use of biomass in winters is found to reduce both the storage and power generation requirements.The results show that the available underground gas storage capacity in the Netherlands is more than required to meet the Dutch electrical demand, with systems using any of the three fuels. However, the renewable power capacity planned for 2030 in the Netherlands will not be sufficient to meet the projected electricity demand in 2030, irrespective of the fuel used for storage. Under these conditions, the system which uses the power capacity most efficiently would be favorable for 2030, which is the hydrogen system.This study compares different fuels from a thermodynamics (exergy) and system sizing perspective. In addition to detailed exergy analysis, further comparative studies will be needed from techno-economic, safety, and social acceptance perspectives before a definitive choice can be made. This study also considers only electrical demand, while the gas storage capacity in the Netherlands might be sufficient to meet other energy demands (heat, transport, industrial feedstock) as well. A study considering other energy demands can be part of future work.With these considerations in mind and with the current design choices, based on current plans for growth of renewables, we find that hydrogen is the most suitable fuel for rSOC-based energy storage in the Netherlands.

Energy Forecasting in Buildings Using Deep Neural Networks

Abstract The building sector is responsible for the largest portion of the total energy consumption in the United States. Conventional physics-based building energy models (BEMs) consider all of the building characteristics in order to accurately simulate their energy usage, requiring an extensive, complex, and costly process, particularly for existing buildings. In recent years, data-driven models have emerged as an additional path toward the prediction of energy consumption in buildings. The purpose of this work is to present a methodology for predicting the energy consumption of buildings using deep neural networks (NNs). Three machine learning algorithms, including a linear regression model, a multilayer perceptron NN, and a convolutional NN (CNN) model, are proposed to solve an energy consumption regression problem using outside dry-bulb temperature as the only input. To assess these methods, a building in Melbourne, FL, is considered and modeled in EnergyPlus. Ten years of data were used as inputs to the EnergyPlus model, and the energy consumption was calculated accordingly. The input to the machine learning algorithm (average daily dry-bulb temperature) and the output (daily total energy consumption) are used for training. Cross-validation was performed on the trained model using actual weather data measured onsite at the building location. The results showed that all three proposed machine learning algorithms were trained successfully and were able to solve the regression problem with high accuracy. However, the CNN model provided the best results when compared with the other two methods. This work also investigates different data filtering techniques that provide the best positive correlation between inputs and outputs for a similar type of problem. Results from this work aim to be used toward accurate energy forecasting that facilitates achieving higher energy efficiency in the building sector. The presented framework provides a readily simple model that allows accurate prediction of outputs when supplied with new inputs and can be used by a wide range of end users.

Theoretical Evaluation of Solar Assisted Desiccant Cooling System for a Small Meeting-Hall

The performance of a solar assisted desiccant cooling system for a meeting-hall located in the College of Engineering/University of Baghdad was evaluated theoretically. The system was composed of four components; a solar air heater, a desiccant dehumidifier, a heat exchanger and an evaporative cooler. A computer simulation was developed by using MATLAB to assess the effect of various design and operating conditions on the performance of the system and its components. The actual weather data on recommended days were used to assess the load variation and the system performance during those days. The radiant time series method (RTS) was used to evaluate the hourly variation of the cooling load. Four operation modes were employed for performance evaluation. A 100 % ventilation mode and 3 recirculation modes, 30 % , 60 % and 100 % recirculation of room air. The concept of variable air volume was employed as a control strategy over the day, by changing the supply airflow rate to match the variation in the cooling load.The results showed that the reduction in moisture content at regeneration temperatures from 55 o C to 75 o C lead to adequate removal of the high latent load in the meeting-hall. Also, the 30 % recirculation of return air resulted in comfortable indoor conditions satisfying the ventilation requirements for most periods of system operation. In addition, the COP of the system was high compared with the conventional vapor compression system. It varied from 1 to 13, when considering solar energy used to regenerate thedesiccant material as free energy.

Operational risk assessment on power system based on weather regionalization considering power ramp of renewable energy generation

With the ever-increased installed capacity of renewable energy generation (REG) units, the power ramp of REG has become a common event, introducing an operational risk to the power system. In order to quantify the operational risk, this paper proposes a novel method for assessing the operational risk of power systems with a high proportion of renewable energy, taking into account the power ramp of REG. Firstly, a weather regionalization is proposed to divide the region of the power system into multiple areas, and then the weather data from the weather stations in these areas will be utilized to forecast the power of REG probabilistically. Moreover, an operational risk assessment model based on the cumulative prospect theory is built to reflect the irrational psychological factors of dispatchers, and two typical indices are introduced to show the operational risk of renewable energy curtailment and load curtailment caused by the power ramp of REG. Finally, numerical simulations are conducted in the actual CEPRI-RE power system in the northwest region of China, and the actual weather data from the meteorological agencies is used. The results show that the proposed method is able to assess the operational risk of the power system with a high proportion of renewable energy effectively, and achieve the early warning 30 min in advance. Besides, it can be found that the power system at the turning point between the power ramp and normal operation has a higher operational risk.

Machine learning model for transient exergy performance of a phase change material integrated-concentrated solar thermoelectric generator

Despite the merits of incorporating phase change materials in concentrating solar thermoelectric generating systems, the following research gaps still need to be filled to make the technology viable. Past research efforts focused on pulsed rectangular and sinusoidal heat flux forms which differ from actual transient weather data obtainable under field exposure. Additionally, a comprehensive exergy performance modeling of the system is yet to be presented in the literature, hence, there is no known thermodynamic implication of incorporating phase change materials in thermoelectric systems from a first and second law perspective. Finally, optimization insights obtained for designing these systems are based on inefficient, computationally expensive, and time-consuming numerical solvers or experimental methods which significantly hinder the ease at which useful design insights are obtained. This work aims to fill in these gaps by presenting the first-ever exergy performance investigation of a paraffin wax integrated thermoelectric generating system using data-driven surrogate machine learning models trained with numerically generated data computed from actual 12 h transient timeseries weather data collected every minute and averaged for a 10-year period (2010–2020). Results show that for an optical concentration of 10, the incorporated system with phase change material provides a 25%, 57%, 25%, and 56% improvement in the thermoelectric temperature difference, power generation, exergy efficiency, and thermodynamic irreversibility of the stand-alone system, respectively, without phase change material. Furthermore, the incorporated system generates electricity for 2 more hours into the night when solar irradiance is unavailable. Finally, the cheap surrogate machine learning model accurately predicts the thermodynamic performance of the stand-alone and incorporated systems with an almost zero error and almost unity coefficient of determination in just 5 s after the numerical method has taken 24 h to generate the data, indicating a 17,280-computation time saving.

Precipitation forecast with logistics regression methods for harvest optimization

This paper proposes a model that forecasts the weather and then, based on that forecast, uses an income-oriented linear programming method to optimize the harvesting process. Data representing a total yearly output capacity of 472,878 tons from 214 different field locations were used to test the model for sugar beet production. Prior to optimization, long-term one-year weather rainfall forecasting was done using 10 years of actual weather data for the field locations. Weather precipitation was forecasted using logistic regression with an accuracy of 84.16%. The outcome of the weather precipitation prediction model was a parameter in the optimization model. The weather forecast for precipitation led to the 120-day harvest planning being optimized. Comparative analysis was done on the outcomes of the developed model and the current scenario. Comparing the current situation to the proposed one, revenue would have increased by 16.7%. Given that it incorporates weather forecasts into the harvest optimization process, the methodology presented in this paper is more practical than other harvest optimization models.

Confronting climate change and livelihood: smallholder farmers’ perceptions and adaptation strategies in northeastern Burundi

Rain-fed agriculture is the main source of livelihood for most of Burundi’s population, especially in the northeastern part of the country. This research is aimed at examining how smallholder farmers in the Northeastern region of Burundi perceive climate change and variability and at identifying the methods that are used to adapt, based on data from 200 small farmers and on actual weather data recorded between 1986 and 2017. We find that the majority of farmers (54%) perceive significant increases in temperature and unpredictability of rainfall duration and intensity and are making adjustments to adapt their agriculture in response to changes in climate. Over 80% of farmers have implemented at least one adaptation strategy among the nine evaluated. Changing crop varieties, changing fertilizers, and planting shade trees are the main adaptation strategies that were being implemented by farmers across the study area. The results of a binary regression model showed that the agricultural education and experience of farmers, as well as farm and family size, livestock ownership, climate information access, credit access, and farm income, strongly influence smallholder farmers’ decisions to adapt to climate change. The main obstacles are the lack of information on climate and adaptation strategies, and poverty, which makes it difficult to cope with the increased costs of farming. Understanding farmers’ perceptions of climate change and variability on a local level would provide information on how to develop adaptation strategies. The present study suggests the need for strengthening farmers’ capacities and improving the policy framework for adaptation to climate change in order to improve farmers’ livelihoods. Implications for policymakers will, therefore, include making flexible credit facilities, and investing in training extension agents on both climate change outreach and coping strategies.