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The development of novel technologies to mitigate the effects of climate change through Smart Grids requires energy related data. Unfortunately, this type of data is not always available in Mexico, especially from non-large urban areas and at the household level. Therefore, we present a dataset that contains electrical demand and consumption time series of 5 households within a small community in Mexico, at various resolutions, as well as weather data. The electrical demand is given in 15 min resolution, while the electrical consumption is presented in both hourly and daily resolutions. The data is contained within 15 separate .csv files; one for each household's resolution. In turn, the weather data is given in two .csv files (for outdoor and indoor variables, respectively) that together contain 24 meteorological variables measured in a 5 min resolution that is not always consistent. The dataset comprises of two separate folders that contain either the electrical demand and consumption files or the weather files. This dataset could aid in the development of novel smart grid methods and algorithms that might be able to push the energy transition in Mexico and other developing countries forward., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2023 The Author(s).)

At the centre of Reunion Island, a region denoted « Mafate » is non-connected to the main electrical grid. In the context of the "Micro Reseau Mafate" research project, financed by the European Regional Development Fund, the PIMENT Laboratory and the local electrification union SIDELEC aim to develop microgrid stations in this isolated area. More than 300 homes are concerned by this alternative solution that these microgrids represent. Presented data come from an experimental microgrid between 3 homes at the place called « Roche Plate », where electrical production is obtained by photovoltaic panels and storage by batteries. Local data are collected to simulate electrical systems and determine the optimal working point of energy distribution. These data concern the meteorological conditions, the electrical performance of devices and the inhabitants' usage of appliances. A weather station placed on the solar plant gives the meteorological conditions of this enclave region. Daily loads for the three families are measured and all the data are stored for analysis. A continuous period of one year of measurements is available for the community and is shared throughout this data paper. These datasets will be completed later. Electrical and meteorological data are collected in situ and stored in a central computer server located at the university., (© 2023 The Author(s).)

The need to reduce energy consumption in buildings is an urgent task. Increasing the use of calibrated building energy models (BEM) could accelerate this need. The calibration process of these models is a highly under-determined problem that normally yields multiple solutions. Among the uncertainties of calibration, the weather file has a primary position. The objective of this paper is to provide a methodology for selecting the optimal weather file when an on-site weather station with local sensors is available and what is the alternative option when it is not and a mathematically evaluation has to be done with sensors from nearby stations (third-party providers). We provide a quality assessment of models based on the Coefficient of Variation of the Root Mean Square Error (CV(RMSE)) and the Square Pearson Correlation Coefficient (R2). The research was developed on a control experiment conducted by Annex 58 and a previous calibration study. This is based on the results obtained with the study case based on the data provided by their N2 house.

Weather is affected by a complex interplay of factors, including topography, location, and time. For the prediction of temperature in Korea, it is necessary to use data from multiple regions. To this end, we investigate the use of deep neural-network-based temperature prediction model time-series weather data obtained from an automatic weather station and image data from a regional data assimilation and prediction system (RDAPS). To accommodate such different types of data into a single model, a bidirectional long short-term memory (BLSTM) model and a convolutional neural network (CNN) model are chosen to represent the features from the time-series observed data and the RDAPS image data. The two types of features are combined to produce temperature predictions for up to 14 days in the future. The performance of the proposed temperature prediction model is evaluated by objective measures, including the root mean squared error and mean bias error. The experiments demonstrated that the proposed model combining both the observed and RDAPS image data is better in all performance measures for all prediction periods compared with the BLSTM-based model using observed data and the CNN-BLSTM-based model using RDAPS image data alone.

In the UK, BB101 is the guidance document for ventilation design of school buildings. There are significant changes proposed in the new version of BB101. The aim of this paper is to examine the requirements of thermal comfort and CO2-based indoor air quality using both versions on a typical naturally ventilated preparatory school design using dynamic thermal simulations. The findings indicate that the new set of requirements on this school building design (both thermal and CO2 concentration) are much more difficult to meet than the requirements from the old version. One of the new thermal comfort criteria may be too difficult to achieve in practice, as the target value was exceeded for all the rooms of the examined design, using both test reference year and design summer year weather data. The ventilation provision for the school design is believed to be adequate. With appropriate ventilation control strategies, the design is able to meet the revised CO2 concentration criteria. Further examinations of the criteria from the new guidance document are needed to make sure the chosen criteria are fit for purpose. The use of future projected design summer year weather data (2020) also adds extra challenges for the preparatory school building to meet the newly proposed adaptive thermal comfort criteria. Practical applications: The research presents a very first assessment of a preparatory school building design using the newly proposed BB101 guidance document. It will assist further exploration on the appropriateness of the new assessment criteria and the use of design summer year weather data in order to explore the implications of the new BB101 guidelines for designers. The method adopted in the research can also be used for other building types to assess overheating in buildings when adaptive comfort criteria are recommended. [ABSTRACT FROM AUTHOR]

• Thermal energy demand evaluation for buildings with high-energy performance. • Application of the Degree Days method for the evaluation of building heating load. • Several dynamic building simulations located in different cities were considered. • A comparison of the results obtained using different weather data sets was done. • Reliable relationships for preliminary of heating demand assessment were provided. The weather is one of the main factors to consider when designing a building because it represents the most important boundary condition to affect the dynamic behaviour of the building. In the literature, many studies use the degree day to predict building energy demand. However, linking the results obtained from a generic building simulation tool with defined degree days, will not give reliable energy evaluation. The goal of this study is to demonstrate that the assessment of building energy demand through the use of the degree day is correct only if the determination of the climate index is a function of the same weather data. The relationship between Heating Degree-Day and heating energy performance was identified by determining some simple correlations, in order to obtain a preliminary evaluation of energy demands. The authors used Heating Degree Days based on three climate data-sets, developing different relationships and feedback. For the extraction of these correlations, numerous dynamic simulations on non-residential buildings characterized by high-energy performance were carried out. From the analysis of the results, it is clear that the relationships with higher correlation coefficients (higher than 0.9) are those that are a function of the degree calculated from the same climatic file used during the simulations. The proposed methodology, validated in this work for an Italian case study can be extended to any country and can be used to improve the reliability of any decision support tool based on climatic indexes. [ABSTRACT FROM AUTHOR]

Statistically interpolated weather station data, outputs from climate reanalyses, and results from downscaled general circulation model (GCM) simulations are widely used to drive a variety of agro-ecosystem model applications, including regional- and national-scale crop modeling. In this study, we compared these three types of gridded weather datasets (total of nine datasets) with actual point-level weather station observations and analyzed the biases in predicted ecosystem variables of evapotranspiration (ET), crop grain yield, soil organic carbon (SOC) change, and soil N2O emissions using the process-based DayCent ecosystem model. As a reference system, we defined continuous corn cropping systems for three different regions in the U.S. Our results suggested that the predicted ecosystem variables can be highly sensitive to the sources of weather input data. Interpolated weather data from the PRISM and Daymet data products provided relatively accurate estimations of important ecosystem variables. Compared with the bias-corrected constructed analogs (BCCA) method, GCM results downscaled with the multivariate adaptive constructed analogs (MACA) method performed better for agro-ecosystem simulations under climate change; for datasets using BCCA, the rainfall frequency was positively biased and likely caused models to significantly underestimate solar radiation. For regional climate change studies that use a baseline simulation (historical period) for comparison, we suggest including the uncertainty of the baseline due to biases in the weather data in addition to the uncertainty in the projected weather data. [ABSTRACT FROM AUTHOR]

On top of climate change and its consequent temperature rises, urban areas have the added burden of the urban heat island (the urban area being warmer than the rural area especially at night under calm, cloud-free conditions). The urban heat island intensity (the difference between the rural air temperature and that in the city centre) can be as large as 10K for the major cities such as London. The urban heat island intensity, consequently, can have a significant effect on the sizing of heating, ventilating and air-conditioning plant and its energy consumption. At present, designers have access to empirical factors for design days only in June, July and August from the Chartered Institution of Building Services Engineers Guide. Or they can use the latest Design Summer Year which implicitly includes the urban heat island intensity. However, the empirical model discussed in this paper allows the designer to add on the hourly urban heat island intensity for central London to any recent year's hourly weather data set from London Heathrow or Bracknell, a more rural site. The model is similar to one for Manchester, suggesting that the model may well be of application to other UK cities.Practical applications: Most buildings that building services engineers and other building designers are involved with are in urban or city centres. However, the weather data for their designs are based on near-rural weather data, which does not include the urban heat island effect. This paper describes the urban heat island effects that a designer needs to consider and the adjustments that can be made, related to London. [ABSTRACT FROM AUTHOR]

Maintenance spare parts demand forecasting is an important foundation of maintenance spare parts inventory control, which is an essential responsibility for managers of an automobile 4S shop. Although the existence of the effect of weather conditions on maintenance spare parts demand has been verified, the study on maintenance spare parts demand forecasting for an automobile 4S shop considering weather data has not been found. In this paper, a novel method is proposed for maintenance spare parts demand forecasting for an automobile 4S shop considering weather data. By taking into account three dimensions of weather data (i.e., temperature, visibility, and slipperiness) and delayed effects of weather conditions on maintenance spare parts demand, a vector of numerical weather data with 30 weather factors is constructed to represent the related weather conditions of a given day. Then, a back propagation (BP) neural network is trained and the weights of the 30 weather factors are determined. Similar historical cases of the target case are extracted, and two forecasting models are respectively trained based on extreme learning machine (ELM) and support vector machines (SVM) using the similar historical cases. The final forecasting model is determined by comparing the fitting precisions of the two forecasting models. The experimental study is conducted based on the real data of an automobile 4S shop. The results show that weather data is critical to maintenance spare parts demand forecasting for an automobile 4S shop, and the extraction of similar historical cases is an effective approach to capture the complex effect mechanism of weather data on maintenance spare parts demand. [ABSTRACT FROM AUTHOR]

Abstract It is increasingly significant to predict building energy consumption in energy-saving decision making. This paper presents the method of support vector regress (SVR) to forecast building energy consumption in southern China. To improve the reliability of SVR in building energy consumption prediction, multiple parameters including weather data such as yearly mean outdoor dry-bulb temperature, relative humidity and global solar radiation and economic factors such as the ratio of urbanization, gross domestic product, household consumption level and total area of structure are taken as inputs. The performance of SVR with respect to two parameters, c and g , is explored using the k -fold cross validation with grid searching method based on radial-basis function kernel. Finally, the performance of the proposed model is checked by comparing the prediction model with statistic data taken from Chinese National Bureau of Statistics in 4 provinces of southern China using statistical error tests such as the mean square error (MSE) and the squared correlation coefficient (r 2). With the analysis based on statistical error tests, the results show that the SVR method can estimate the building energy consumption with good accuracy with MSE less than 0.001 and r 2 more than 0.99. [ABSTRACT FROM AUTHOR]

Abstract The interest in harnessing renewable sources of energy for space heating in residential applications has increased in recent decades due in part to their cost benefits and an increase in environmental awareness. The proposed system uses shallow ground as an energy reservoir; able to absorb heat during the summer and release it during the winter respectively. This paper presents by means of a mathematical model an analysis of such a system using real temperature profiles and compares them to idealized fitted functions. A validated two-dimensional multiphase model describing mass, momentum, turbulence and conjugate heat transfer between the bayonet tube and the ground is used to compare the effects of using a simplified fitted function to represent the ambient temperature with that of hourly temperature readings from a weather monitoring station. The results indicate that the strong random nature of the temperature variations complicates the analysis. It is shown that fitted functions can over predict the overall performance of the system, however under-predict the performance on the short term due in part to cold snaps, heat waves and variations in temperatures. While the benefits and capabilities of the system under real loads show the potential of the system, work on thermal buffering or real-time intelligent control systems for quality control will be necessary to maintain constant temperature for thermal comfort and optimum energy extraction. [ABSTRACT FROM AUTHOR]

Abstract The need of daily solar radiation (DSR) has been well recognized over the years because many countries consider solar energy into power grids. The essence of this paper is to apply the deep belief network (DBN) to estimating daily solar radiation on horizontal surfaces for Lhasa in China. Measured weather data (wind speed, sunshine duration, air dry-bulb temperature, air relative humidity) which cover the period of 1994–2015 are obtained and devoted to modeling. The statistical indicators (mean absolute bias error (MABE), root mean square error (RMSE) and correlation coefficient (R)) show that, the DBN model, optimized by testing the effect of multiple parameters, has more promising result when compared to back propagation (BP) network. [ABSTRACT FROM AUTHOR]

Abstract Building energy demands in long-term studies are mostly calculated using the averaged weather data sets, reflecting the changes in environmental conditions over time, thus the different energy consumption each year. This paper reviews and discusses the coupled effects of warming trend in global mean surface temperatures, application of different design weather datasets, and utilization of different methods of building energy assessment on the calculated energy demands. The possible inaccuracies of building energy analyses caused by those three factors are investigated on the example of a residential house in Central Europe. For that purpose eight different weather data sets for Prague, Czech Republic are selected and simulations in two different scales are performed. The analysis of the effect of recent weather data is performed by an improved methodology. First, the simulation brings an increased precision as an advanced hygrothermal model is used for energy calculations. Second, the building performance is assessed, contrary to the Czech national standards, using both heating and cooling energy demands. The simulation results confirm the warming trend in the time period of 2013–2017 as the average heating demands are 3.95% lower and the average cooling demands 3.96% higher in a comparison with the Test Reference Year. In the extreme years, a 12–15% decrease of energy consumed for heating and up to 20% increase of energy necessary for cooling is found. This is in accordance with the presumed warming trend that has been widely discussed during the last few decades. Furthermore, the presented results verify the critical and positive design weather years as suitable for application in the simulation of heating and cooling energy demands in the Czech Republic. Highlights • The effect of recent weather data on building energy demands is analyzed. • The average heating demands are 3.95% lower in 2013–2017 period than TRY. • The average cooling demands are 3.96% higher in 2013–2017 period than TRY. • The results confirmed the effect of warming trend in the recent years. [ABSTRACT FROM AUTHOR]

Abstract Though Typical Meteorological Year (TMY) data is widely used for predicting daylight quality and daylight-responsive control system performance, few studies investigated whether the predictions were accurate. In this study, daylight simulation was performed for a real space using the TMY data and an actual local weather data with a one-minute interval. The former was used to predict the daylight quality, to design a closed-loop daylight-responsive dimming control system, to select the calibration hours, and to predict the control system performance; the latter was used to characterize the actual daylight quality and control system performance. Though the actual weather conditions can provide 30% higher daylight levels to the space, which seemed to suggest a potential to achieve a greater energy saving, none of the existing CBDM measures suggested such a difference. The TMY data was used to design a closed-loop dimming control system, to select the appropriate weather conditions for system calibration, and to predict the energy saving of the system by limiting the occurrence of over-dimming below 2% of the occupied hours. It was found the predicted energy saving was generally accurate if the system calibration was performed when the weather conditions were similar to those identified using the TMY data. When the calibration was performed under the inappropriate weather conditions, the system achieved lower energy saving and had a much frequent occurrence of over-dimming conditions. In addition, performing the system calibration under the weather conditions that resulted in higher photosensor signal to workplane illuminance ratios may be helpful to reduce the occurrence of over-dimming conditions caused by the larger variation of the weather conditions in reality. [ABSTRACT FROM AUTHOR]

In this paper, we propose a fully automated machine learning based forecasting system, called Photovoltaic Power Forecasting (PVPF) tool, that applies optimised neural networks algorithms to real-time weather data to provide 24 hours ahead forecasts for the power production of solar photovoltaic systems installed within the same region. This system imports the real-time temperature and global solar irradiance records from the ASU weather station and associates these records with the available solar PV production measurements to provide the proper inputs for the pre-trained machine learning system along with the records’ time with respect to the current year. The machine learning system was pre-trained and optimised based on the Bayesian Regularization (BR) algorithm, as described in our previous research, and used to predict the solar power PV production for the next 24 hours using weather data of the last five consecutive days. Hourly predictions are provided as a power/time curve and published in real-time at the website of the renewable energy center (REC) of Applied Science Private University (ASU). It is believed that the forecasts provided by the PVPF tool can be helpful for energy management and control systems and will be used widely for the future research activities at REC. [ABSTRACT FROM AUTHOR]

Currently studies exploring the effect of real-time traffic and weather characteristics on crash severity are relatively limited, and studies focusing on powered two-wheelers (PTWs) are even fewer. The objective of this paper is to add to current knowledge through the investigation of PTW occupant injury severity on the urban motorway Attica Tollway in Athens, Greece. For that purpose, 163 crashes (23 severe/fatal and 140 slight) that occurred between 2006 and 2011 were analyzed by considering real-time traffic and weather parameters. The required crash data was extracted from the Greek crash database of the National Technical University of Athens and also Attica Tollway. Traffic data were extracted from the Traffic Management Center of Attica Tollway, and weather data were extracted from the Hydrological Observatory of Athens (HOA). To reduce data dimensionality and to overcome multicollinearity between variables, a two-step approach was followed. First, principal component analysis and random forests were used as preliminary analyses. To account for the low number of and proportion of killed and severely injured (KSI) occupants, a Firth logistic regression was then applied. Results showed that in general, traffic and speed variations caused more severe injuries whereas weather parameters seemed to have no effect. It was also found that mean speeds and variations in volumes of trucks increased the crash injury severity of PTW crashes, but it was interesting that an increased proportion of trucks led to slighter injuries. The findings of this paper are considered promising and will contribute to better understanding of occupant injury severity on urban motorways by focusing on PTWs. [ABSTRACT FROM AUTHOR]

Abstract The Wind energy conversion systems (WECS) have been developed in the past few years as renewable energy sources for minimizing greenhouse emissions while reducing power consumption. Despite the importance of modeling and simulation, experimental studies of WECS are required to figure out the behavior of these systems according to the environmental and climatic circumstances (e.g., wind availability). This paper presents the implementation and experimentation of a wind energy conversion model using the permanent magnet synchronous generator (PMSG) according to different wind speeds. Experiments have been conducted with the main goal is to show the accuracy of the Wind turbine model. [ABSTRACT FROM AUTHOR]

Abstract It is increasingly significant to predict building energy consumption (BEC) in energy-saving decision making. This paper presents the method of support vector machines (SVM) to forecast building energy consumption in China. To improve the reliability of SVM in building energy consumption prediction, multiple parameters including weather data such as yearly mean outdoor dry-bulb temperature, relative humidity and global solar radiation and economic factors such as the ratio of urbanization, gross domestic product, household consumption level and total area of structure are taken as inputs. The performance of SVM with respect to two parameters, c and g , is explored using the k -fold cross validation with grid searching method based on radial-basis function kernel. Finally, the performance of the proposed model is checked by comparing the prediction model with statistic data taken from Chinese National Bureau of Statistics in 30 provinces (including autonomous regions and municipalities) of China using statistical error tests such as the mean square error (MSE) and the squared correlation coefficient (r 2). With the analysis based on statistical error tests, the results show that the SVM method can estimate the building energy consumption with good accuracy with MSE less than 1E-3 and r 2 more than 0.991. [ABSTRACT FROM AUTHOR]

Rice is generally grown under completely flooded condition and providing food for more than half of the world’s population. Any changes in weather parameters might affect the rice productivity thereby impacting the food security of burgeoning population. So, the crop yield forecasting based on weather parameters will help farmers, policy makers and administrators to manage adversities. The present investigation examines the application of stepwise multiple linear regression (SMLR), artificial neural network (ANN) solely and in combination with principal components analysis (PCA) and penalised regression models (e.g. least absolute shrinkage and selection operator (LASSO) or elastic net (ENET)) for rice yield prediction using long-term weather data. The R2 and root mean square error (RMSE) of the models varied between 0.22-0.98 and 24.02-607.29 kg ha−1, respectively during calibration. During validation with independent dataset, the RMSE and normalised root mean square error (nRMSE) ranged between 21.35-981.89 kg ha−1 and 0.98-36.7%, respectively. For evaluation of multiple models for multiple locations statistically, overall average ranks on the basis of R2 and RMSE of calibration; RMSE and nRMSE of validation were calculated and non-parametric Friedman test was applied to check the significant difference among the models. The ranking of the models revealed that LASSO (2.63) was the best performing model followed by ENET (3.07) while PCA-ANN (4.19) was the worst model which was found significant at p < 0.001. The reason behind good performance of LASSO and ENET is that these models prevent overfitting and reduce model complexity by penalising the magnitude of coefficients. Then, pairwise multiple comparison test was performed which indicated LASSO as the best model which was found similar to SMLR and ENET. So, for prediction of rice yield, these models can very well be utilised for west coast of India. [ABSTRACT FROM AUTHOR]

With the increase in oil prices, developing nations end up paying expensive electricity and heating bill. This leading study investigates the experimental development of a new energy-saving system by integrating a solar water heater and solar cooling absorption cycle with a conventional boiler for domestic hot water and heating purposes. The heating and cooling load calculations for space heating of the building were calculated using TRNSYS 14.1 computer software and the results were used in calculating the energy-saving value. A 65 flat plate solar collector-chiller system with a total surface area of 130 m2 was integrated with the boiler and used to supply heating and cooling for a three-story building (1500 m2) in Al Bayt University, Jordan. The integrated system helped to save energy, reduced the emission of CO2 into the atmosphere, supplied hot water, and space heating/cooling requirements to the building year-round, and reduced the overall energy cost of heating and cooling by 55% and 48%, respectively. Moreover, the techno-economic analysis showed that the payback period of the combined system with a total cost of $18,650 is roughly 2.5-year. The solar water heating/cooling system has the potential to provide more than 50% of the house energy demand free of charge with a significant reduction in carbon footprint.

The study of power load forecasting is gaining greater significance nowadays, particularly with the use and integration of renewable power sources and external power stations. Power forecasting is an important task in the planning, control, and operation of utility power systems. In addition, load forecasting (LF) aims to estimate the power or energy needed to meet the required power or energy to supply the specific load. In this article, we introduce, review and compare different power load forecasting techniques. Our goal is to help in the process of explaining the problem of power load forecasting via brief descriptions of the proposed methods applied in the last decade. The study reviews previous research that deals with the design of intelligent systems for power forecasting using various methods. The methods are organized into five groups—Artificial Neural Network (ANN), Support Vector Regression, Decision Tree (DT), Linear Regression (LR), and Fuzzy Sets (FS). This way, the review provides a clear concept of power load forecasting for the purposes of future research and study.

Heatwave weather files are needed to assess building performance under future heatwaves. This paper presents a methodology for producing a minimum set of heatwave weather files, which should be representative of the diversity of heatwaves expected in a location of interest. It is a four-step methodology. Weather projections are first collected from the CORDEX project database. Then, heatwaves are identified in the weather data. A list of independent and significant indicators is constructed to characterise the heatwaves. Samples of heatwaves are finally selected based on the list of indicators. The methodology was tested for the location of the Lyon-Saint Exupery Airport. A total of 2229 heatwaves were identified within 1260 years of weather projections. The heatwaves showed a high degree of diversity in terms of weather characteristics. The sampling process selected only 8 representative heatwaves. This number is sufficiently reduced to consider using the reduced set of heatwave weather files for assessing building performance. • A 4-step methodology is built to select a representative set of future heatwaves. • Heatwaves are identified from climate projection of the CORDEX project. • Building EnergyPlus Weather (EPW) file from simulation data of the CORDEX project. • 2229 heatwaves were identified for the location of Lyon Saint-Exupéry Airport. • The sampling process selected only 8 representative heatwaves. [ABSTRACT FROM AUTHOR]

Electricity from renewable energy sources (RES-E) is gaining more and more influence in traditional energy and electricity markets in Europe and around the world. When modeling RES-E feed-in on a high temporal and spatial resolution, energy systems analysts frequently use data generated by numerical weather models as input since there is no spatial inclusive and comprehensive measurement data available. However, the suitability of such model data depends on the research questions at hand and should be inspected individually. This paper focuses on new methodologies to carry out a performance evaluation of solar irradiation data provided by a numerical weather model when investigating photovoltaic feed-in and effects on the electricity grid. Suitable approaches of time series analysis are researched from literature and applied to both model and measurement data. The findings and limits of these approaches are illustrated and a new set of validation indicators is presented. These novel indicators complement the assessment by measuring relevant key figures in energy systems analysis: e.g., gradients in energy supply, maximum values and volatility. Thus, the results of this paper contribute to the scientific community of energy systems analysts and researchers who aim at modeling RES-E feed-in on a high temporal and spatial resolution using weather model data.

Larch is widely used in tree-ring studies, while tree-ring width and signature years provide information on environmental changes with annual resolution. The aim of the study was to build tree-ring (TR), early- (EW) and latewood (LW) width chronologies by larch species (Hybrid, European, Russian, Japanese, Kuril), and to study the response of the radial increment of larches to weather at Järvselja forest stands by using correlation, redundancy analysis, and the pointer year method. The increment cores were collected from 233 trees in Järvselja larch stands during autumn 2014. The increment cores were collected from 24 stands and divided into five groups by larch species. High EPS values (≥ 0.887) for the larch species in all groups indicate that a sufficient number of trees was included in the chronologies to be representable. The results show that different species have a similar sensitivity to various weather variables. Among the factors influencing the size of radial growth of larches were the weather conditions prevailing in the autumn of the previous year and the spring of the current year. An overall RDA revealed that 63.9% of tree growth variation in larch species was explained by the considered weather variables (F = 4.925, p < 0.001). The pointer year analysis distinguished between several common extreme pointer years by larch species; it revealed a significant response to the winter temperature and the temperature in spring. However, these weather characteristics are very complex and the causes that can affect tree growth may vary from year to year.

Building energy simulations are normally run through Typical Weather Years (TWYs) that reflect the average trend of local long-term weather data. This paper presents a research aimed at generating updated typical weather files for the city of Catania (Italy), based on 18 years of records (2002–2019) from a local weather station. The paper reports on the statistical analysis of the main recorded variables, and discusses the difference with the data included in a weather file currently available for the same location based on measurements taken before the 1970s but still used in dynamic energy simulation tools. The discussion also includes a further weather file, made available by the Italian Thermotechnical Committee (CTI) in 2015 and built upon the data registered by the same weather station but covering a much shorter period. Three new TWYs are then developed starting from the recent data, according to well-established procedures reported by ASHRAE and ISO standards. The paper discusses the influence of the updated TWYs on the results of building energy simulations for a typical residential building, showing that the cooling and heating demand can differ by 50% or even 65% from the simulations based on the outdated weather file.

Mapping of meteorological conditions surrounding road infrastructures is a critical tool to identify high-risk spots related to harsh weather. However, local or regional data are not always available, and researchers and authorities must rely on coarser observations or predictions. Thus, choosing a suitable method for downscaling global data to local levels becomes essential to obtain accurate information. This work presents a deep analysis of the performance of two of these methods, commonly used in meteorology science: Universal Kriging geostatistical interpolation and Weather Research and Forecasting numerical weather prediction outputs. Estimations from both techniques are compared on 11 locations in central continental Portugal during January 2019, using measured data from a weather station network as the ground truth. Results show the different performance characteristics of both algorithms based on the nature of the specific variable interpolated, highlighting potential correlations to obtain the most accurate data for each case. Hence, this work provides a solid foundation for the selection of the most appropriate tool for mapping of weather conditions at the local level over linear transport infrastructures.

In this paper, we propose a new temperature prediction model based on deep learning by using real observed weather data. To this end, a huge amount of model training data is needed, but these data should not be defective. However, there is a limitation in collecting weather data since it is not possible to measure data that have been missed. Thus, the collected data are apt to be incomplete, with random or extended gaps. Therefore, the proposed temperature prediction model is used to refine missing data in order to restore missed weather data. In addition, since temperature is seasonal, the proposed model utilizes a long short-term memory (LSTM) neural network, which is a kind of recurrent neural network known to be suitable for time-series data modeling. Furthermore, different configurations of LSTMs are investigated so that the proposed LSTM-based model can reflect the time-series traits of the temperature data. In particular, when a part of the data is detected as missing, it is restored by using the proposed model’s refinement function. After all the missing data are refined, the LSTM-based model is retrained using the refined data. Finally, the proposed LSTM-based temperature prediction model can predict the temperature through three time steps: 6, 12, and 24 h. Furthermore, the model is extended to predict 7 and 14 day future temperatures. The performance of the proposed model is measured by its root-mean-squared error (RMSE) and compared with the RMSEs of a feedforward deep neural network, a conventional LSTM neural network without any refinement function, and a mathematical model currently used by the meteorological office in Korea. Consequently, it is shown that the proposed LSTM-based model employing LSTM-refinement achieves the lowest RMSEs for 6, 12, and 24 h temperature prediction as well as for 7 and 14 day temperature prediction, compared to other DNN-based and LSTM-based models with either no refinement or linear interpolation. Moreover, the prediction accuracy of the proposed model is higher than that of the Unified Model (UM) Local Data Assimilation and Prediction System (LDAPS) for 24 h temperature predictions.

Forecasting yield of crops is important for planners in taking tactical decisions for ensuring food availability. Crop simulation models are useful tool to forecast the crop yield. The CERES-wheat model calibrated and validated with experimental data was used to predict the district wise yield in Bihar. Based on the areas under different dates of sowing and crop cultivars, a correction factor was applied on simulated yield to predict the district wise and regional wheat yield. The simulated yield were higher than the actual yield in all the three agro-climatic zones, while the forecasted yields were very close to actual. The percentage deviation of the forecasted yield from the actual was ±4.0 %. The results clearly indicated that the CERES-Wheat model can be used to regional production estimates of wheat in Bihar.