Forecast Equations Research Articles

Response surface methodology-based optimisation of adsorption of diclofenac and treatment of pharmaceutical effluent using combined coagulation-adsorption onto nFe2O3 decorated water chestnut shells biochar.

This work involved the preparation of pristine and iron nanoparticle-loaded biochar from a water chestnut shell to remove diclofenac sodium (DCF) containing effluent of pharmaceutical origin. To create suitable forecasting equations for the modelling of the DCF adsorption onto the adsorbent, response surface methodology (RSM) was used. The parameters, e.g. pH, adsorbent mass, DCF concentration and contact time, were used for the modeling of adsorption. The RSM model predicts that for 98.0% DCF removal, the ideal conditions are pH 6, an adsorbent dose of 0.5 g L-1, and a contact time of 60 min with an initial adsorbate concentration of 25 mg L-1 at 303 K. The maximum capacity deduced from the Langmuir model was 75.9 mg g-1 for pristine water chestnut shell biochar (pWCBC) and 122.3 mg g-1 for magnetically modified nano-Fe2O3 biochar (mWCBC). Under equilibrium conditions, the Langmuir model was the best-suited model compared to the Temkin and Freundlich models. The adsorption data in this investigation efficiently fitted the pseudo-second-order model, emphasizing that chemisorption or ion exchange processes may be involved in the process. The WCBC demonstrated recyclability after 10 cycles of repeated adsorption and desorption of DCF. A combined coagulation adsorption process removed COD, NH3-N, NO3-, PO43-, and DCF by 92.50%, 86.41%, 77.57%, 84.54%, and 97.25%, respectively. This study therefore shows that coagulation followed by adsorption onto biochar can be a cost-effective substitute for conventional pharmaceutical wastewater treatment.

IS THE PHILLIPS CURVE USEFUL FOR FORECASTING INFLATION IN RUSSIA?

The paper analyzes the experience of using the Phillips curve to model inflation in Russia, taking into account the specific features of domestic labor market. Based on Russian data for the period from 2000 to 2022, a wide range of inflation forecasting models has been built: both based on the Phillips curve and alternative ones. The econometric tools used are autoregression models with a moving average in the residuals taking into account seasonality (SARIMA) and their generalizations; autoregression models of distributed lags (ADL) and their generalizations; as well as other estimation methods. During the simulation, we use data on inflation, inflation expectations, production dynamics, unemployment, wages, exchange rates, money supply and other variables. The models are compared drawing on the accuracy of single-period and multi-period out-of-sample forecasts. The modelling results allows us to conclude that onedimensional models work well during the periods of stable economic dynamics, but lose in their predictive power to the “triangular” Phillips curve in crisis years. Comparison of models for forecasting inflation shows that in a stable economic situation, one-dimensional models provide a more reliable forecast. However, in the context of structural transformation faced by the Russian economy in 2022, the “triangular” models of the Phillips curve demonstrate maximum quality of the forecast. Although the acceleration of inflation in 2022 obviously reduces the accuracy of any forecast equations, however, the “triangular” model based on the lags in inflation, unemployment and the index of industrial production demonstrates the best results. This conclusion remains stable to changes in the length of the time series used for forecasting, as well as to changes in forecasting horizon.

Carbon Footprint Assessment Based on Life Cycle Assessment of Biomass Power Plant

This research aims to study the carbon footprint based on the life cycle assessment of biomass power plants. Covering the acquisition of raw materials to the end of the production process in biomass power plants (cradle to grave: C2G). The study proposes constructive ways to reduce the carbon footprint of biomass power plants. Conducted research consisting of 3 phases: phase 1, the rubber plantation process; phase 2, the rubber wood processing plant; and phase 3, the biomass power plant. Assessment of carbon footprint: following the principle of life cycle assessment, the results of the carbon footprint assessment found that in Phase 1, the process of growing rubber trees throughout the life cycle calculated the carbon footprint of the sample group as average greenhouse gas emissions from fresh rubber wood and combined with the average greenhouse gas emissions from rubber wood timbers of 1.1186 kgCO2eq per day. In phase 2, rubber wood processing plants throughout the life cycle have average greenhouse gas emissions of 15,319.11 kgCO2eq per day. In phase 3, biomass power plants electricity capacity is 9.9 MW per day, and their greenhouse gas emissions are 44,753.60 kg CO2eq per day. The result found that greenhouse gas emissions from biomass power plants per 1 kWh accounted for a carbon dioxide equivalent of 4.52 kgCO2eq. Calculate the predictor factors affecting the amount of greenhouse gases in biomass power plants in raw score form as Y = 373.516 +.082 (raw material quantity), where Y is the greenhouse gas emission (kgCO2eq), using the forecast equation in standard score form. With Z =.983raw material quantity3, It was found that the quantity of raw materials had a positive correlation with the quantity of greenhouse gases at a statistical significance of .01 with a correlation coefficient of .983, which could explain the variability of the variables of the quantity of greenhouse gases with 96.70 percent accuracy.

Evaluation of Empirical Equations and Machine Learning Models for Daily Reference Evapotranspiration Prediction Using Public Weather Forecasts

Although the studies on model prediction of daily ETo based on public weather forecasts have been widely used, these studies lack the comparative evaluation of different types of models and do not evaluate the seasonal variation in model prediction of daily ETo performance; this may result in the selected model not being the best model. In this study, to select the best daily ETo forecast model for the irrigation season at three stations (Yinchuan, Tongxin, and Guyuan) in different climatic regions in Ningxia, China, the daily ETos of the three sites calculated using FAO Penman–Monteith equations were used as the reference values. Three empirical equations (temperature Penman–Monteith (PMT) equation, Penman–Monteith forecast (PMF) equation, and Hargreaves–Samani (HS) equation) were calibrated and validated, and four machine learning models (multilayer perceptron (MLP), extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), and gradient boosting with categorical features support (CatBoost)) were trained and validated against daily observed meteorological data (1995–2015 and 2016–2019). Based on public weather forecasts and daily observed meteorological data (2020–2021), the three empirical equations (PMT, PMF, and HS) and four machine learning models (MLP, XGBoost, LightGBM, and CatBoost) were compared in terms of their daily ETo prediction performance. The results showed that the daily ETo performance of the seven models in the irrigation season with a lead time of 1–7 days predicted by the three research sites decreased in the order of spring, autumn, and summer. PMT was the best model for the irrigation seasons (spring, summer, and autumn) at station YC; PMT and CatBoost with C3 (Tmax, Tmin, and Wspd) as the inputs were the best models for the spring, autumn irrigation seasons, and summer irrigation seasons at station TX, respectively. PMF, CatBoost with C4 (Tmax, Tmin) as input, and PMT are the best models for the spring irrigation season, summer irrigation season, and autumn irrigation season at the GY station, respectively. In addition, wind speed (converted from the wind level of the public weather forecast) and sunshine hours (converted from the weather type of the public weather forecast) from the public weather forecast were the main sources of error in predicting the daily ETo by the models at stations YC and TX(GY), respectively. Empirical equations and machine learning models were used for the prediction of daily ETo in different climatic zones and evaluated according to the irrigation season to obtain the best ETo prediction model for the irrigation season at the study stations. This provides a new idea and theoretical basis for realizing water-saving irrigation during crop fertility in other arid and water-scarce climatic zones in China.

Long-term probabilistic streamflow forecast model with “inputs–structure–parameters” hierarchical optimization framework

Accurate, reliable, and stable streamflow forecasts are essential for risk assessment and decision making in water resources management. Owing to the limited value of deterministic forecasting, probabilistic forecasting incorporating probability and confidence intervals is achieved by optimizing the predictors and forecast equation while identifying forecast error features. The combination of deterministic forecasts and error identification can facilitate water resources management. This study improved the traditional forecast model’s inputs selection, structure designation, and error parameters calibration to propose a long-term probabilistic streamflow forecast model with hierarchical optimization of the predictors, forecast equation, and errors characteristics. The model develops information entropy theory to screen the driving predictor set, the long short-term memory (LSTM) model to conduct deterministic forecasts, and the generalized autoregressive conditional heteroskedasticity (GARCH) model to identify time-varying errors. The proposed model, used in some case studies to forecast the monthly streamflow of two lakes, Hongze and Luoma lakes in China, was evaluated using the multidimensional index method considering “accuracy–reliability–stability” performance. The results revealed the following: (1) Predictor screening based on information entropy investigates the statistical characteristics of predictors, thereby improving the reliability of streamflow forecasts. (2) The LSTM model exploits the response between the driving predictors and streamflow, whereas GARCH model identifies the time-varying features of forecast errors effectively, which reduces the probability of forecast failure and increases the accuracy and stability of probabilistic forecasts. (3) Under current meteorological observation conditions and forecasting capability, the proposed forecasts can extend the maximum forecast lead time from 1 month to 3 months. (4) The proposed model improves the accuracy (root mean square error: 6.7%–34.8%), reliability (Brier score: 15.3%–27.9%), and stability (mistaken distance: 36.4–52.6%) in comparison with the benchmark of the two case studies, indicating that “inputs–structure–parameters” hierarchical optimization provides effective forecast information for water resources management.

Teacher Motivation and Morale Influencing the Effectiveness of Bangkok Metropolitan Administration Schools

This study aimed to examine the teacher motivation and morale in Bangkok Metropolitan Administration schools, the school effectiveness, the relationship between the factors of teacher motivation and morale in performing their jobs that influence school effectiveness, and the development of guidelines for enhancing teacher motivation and morale in relation to school effectiveness. In the study, the researcher employed a mixed research methodology. In the initial phase, questionnaires were used to collect data. The population consisted of Bangkok Metropolitan Administration school teachers who served their duty in 2022. A multistage random sampling selected 375 persons in total. Mean, percentage, and standard deviation were applied as descriptive statistics. In the last phase, the researcher conducted in-depth interviews with seven experts selected through purposive sampling to gather their perspectives on the applicability, possibility, and usefulness of the the guidelines for enhancing teacher motivation and morale in relation to school effectiveness. The data was analyzed using a content analysis method. According to the findings, the multiple correlation coefficient was .760 (R = 0.760 at the .05 level of significance. The predictive coefficient or predictive power of 57.7 percent (R2 = 0.577), with the regression coefficient () arranged in descending order: 1) Professional Success (β=0.321) 2) Career Growth (β=0.238) 3) School Policies (β=0.162) 4) Workplace atmosphere and environment (β=0.102) 5) Governance Aspects (β=.096). The forecast equations can be generated using the regression coefficients of the predictors in raw score (b) and standard score () as follows: In raw score (unstandardized score) form, the forecast equation is Y' = 1.391 + 0.255 (x 6 )0.183 (x 10 x 10) 0.124 (x 5 (x 5 )0.050 (x 3 (x 3 )0.075 (x 2 (x 2). Standardized score forecast equations Zy = 0.321 (x 6) + 0.238 (x 10) + 0.162 (x 5) + 0.102 (x 3) + 0.096 (x 2). The researcher also devised a guideline containing nineteen recommendations for enhancing the top five teacher motivation and morale factors that influence school effectiveness. There are 19 guidelines for improving teacher morale, which affects school effectiveness.

Factors Related To Good Mental Health During Study Of Students Rajabhat University Group During The Learning, New Normal Era, Thailand

The purpose of this research was to assess the mental health index of students. And to find factors related to the mental health of students. The sample consisted of 441 students by multistage randomization. The tools used to collect data included questionnaires, mental health indicators. The confidence value was .88 and the open-ended questionnaire. Data were analyzed with descriptive statistics. Pearson's correlation analysis. Generate forecast equations using stepwise multiple regression analysis. The qualitative data used category and content analysis. The results showed that: 1) Most of the students had better mental health than the general population, 399 or 94.77 percent, followed by mental health equal to 20 people, or 4.75 percent, and the lowest mental health was lower than the general population. There were 2 people, representing .48 percent, respectively. 2) Internal variables that were positively correlated with mental health of the students were: 3rd year students of the Faculty of Education Student age variables It was found that the first year student variable of the faculty of nursing had a negative relationship with mental health better than the general population of the students. 3) The positive feeling from the teaching and learning management policy applied to students is have relieved stress feel more rested get more sleep being with family more time for other activities. You don't have to meet a lot of people, reducing your risk. Save on travel and living expenses feel more sympathetic to parents learn new things, be happy, and do not rush to wake up to study at the university. Study anywhere more time for hobbies, less traveling. And can wake up late. 4) The negative feelings from the teaching and learning policy applied to students were away from home, risk of contracting disease, not going out to do bird activities at shelter. Have to wear a mask all the time, get angry easily, unclear in some things. Tired of working, little free time, not eating and sleeping on time doing anything is more difficult when doing activities cannot be done to the fullest. Less time, anxiety and unprepared ness and rarely have friends.

Establishment and Application of Foshan Ozone Concentration Forecast Equation

The formation and changes of ozone (O3), a secondary pollutant in the atmosphere, are complex, and ozone forecasting has become one of the current problems in air pollution prevention and control. In this study, the relationships between the near-surface O3 concentration and meteorological elements (high- and low-level) in Foshan from 2014 to 2017 were analyzed, and the concentration forecasting equation was established, tested, and applied. The results showed that the near-surface O3 changed closely related to high- and low-level meteorological elements. Meteorological elements such as temperature and sunshine hours were significantly positively correlated with O3 concentration, whereas relative humidity, total (low) cloud cover, and wind speed were negatively correlated with O3. Heavy O3 pollution often occurred with meteorological conditions of low wind speed, sunny days and few clouds, low relative humidity, longer sunshine time, and higher temperature. The definitions of high-concentration O3 potential index (HOPI) and wind direction index (WDI) in the Foshan area could better characterize the meteorological conditions of O3 pollution. Considering 13 meteorological elements, such as HOPI and WDI at different heights, the O3 concentration forecasting equation in the Foshan area was established using multi-indicator stacking and multiple stepwise regression methods. Using the 2018 data, it was found that the correlation coefficient R between the simulated values and the measured values reached 0.82, and the forecast equation had a good fitting effect and predictability.

Study on the Dynamic Change of Regional Water Level and Climate Based on Forecast Equation

Abstract Because there are too many factors affecting the change of hydrometeorological elements in the long term, it is necessary to use a single linear relationship to construct the forecast equation, which will omit too many forecast factors. Therefore, it is necessary to use a variety of nonlinear function types of correlation coefficient calculation and comparison, clear optimization of forecast factors to build the corresponding forecast equation. In this paper, we study spatial change of water in the understanding of trend test principle, analysis method and dynamic time series model, on the basis of based on regional water level change to build the corresponding forecast equation, the design and implementation of the regional water level change and climate dynamics forecasting system software, the empirical analysis found that the regional water level has a close relationship between dynamic change and climate change.

Explainable inflation forecasts by machine learning models

Forecasting inflation accurately in a data-rich environment is a challenging task and an active research field which still contains various unanswered methodological questions. One of them is how to find and extract the information with the most predictive power for a variable of interest when there are many highly correlated predictors, as in the inflation forecasting problem. Traditionally, factor models have been used to tackle this problem. However, a few recent studies have revealed that machine learning (ML) models such as random forests may offer some valuable solutions to the problem. This study encourages greater use of ML models with or without factor models by replacing the functional form of the forecast equation in a factor model with ML models or directly employing them with several feature selection techniques. This study adds new tree-based models to the analysis in the light of the recent findings in the literature. Moreover, it proposes the integration of feature selection techniques with Shapley values to find out concise explanations of the inflation predictions. The results obtained by a comprehensive set of experiments in an emerging country, Turkey, facing a high degree of volatility and uncertainty, indicate that tree-based ensemble models can be advantageous by providing better accuracy together with explainable predictions.

Prediction of Load Capacity in Microgrid by Multiple Regression Method

The article presents the results of load power forecasting in Microgrid systems by multiple regression with a forecast range of one day. energy sources, as well as tools for storage, redundancy and load management. The design and construction of such systems is cost-effective, as these systems are powered by renewable energy sources, which is attractive due to subsidies and discounts on energy distribution - the so-called "green tariff". depends on weather conditions, such as temperature, pressure, humidity, wind speed and direction, cloudiness, etc., the task of predicting the load capacity depending on environmental parameters is relevant. Therefore, a forecast model of load capacity based on environmental data is developed and its software implementation is given. The daily curves of changes in load power with a discreteness of one hour are presented. Daily curves of load capacity changes on weekdays and weekends are also provided. A free resource has been selected to download the environmental database. A specific day is set for load forecasting. Hourly values ​​of environmental data (temperature, pressure, humidity) for a given day are given. The criteria for finding such days according to the environmental data are selected and the allowable percentage difference of mathematical expectation and variance of the relevant data is established. The parameters of mathematical expectation and variance of a given day are calculated. The statistical dependence between load data and environmental data is calculated. Regressive equations of the found similar days are constructed, on the basis of which the regressive forecast equation of loading capacity for days ahead is received. The daily curve of the forecasted load is presented and the comparative schedule of the forecasted with the real value of the load is constructed. The accuracy of the prediction is estimated using the average absolute error of MAPE. The algorithm and results of work of the developed program on which search of a similar day and calculation of forecast value for forecasting of power of loading for days ahead are represented are resulted.

A Comparative Study of Time Series Forecasting of Solar Energy Based on Irradiance Classification

Sustainable energy systems rely on energy yield from renewable resources such as solar radiation and wind, which are typically not on-demand and need to be stored or immediately consumed. Solar irradiance is a highly stochastic phenomenon depending on fluctuating atmospheric conditions, in particular clouds and aerosols. The complexity of weather conditions in terms of many variable parameters and their inherent unpredictability limit the performance and accuracy of solar power forecasting models. As renewable power penetration in electricity grids increases due to the rapid increase in the installation of photovoltaics (PV) systems, the resulting challenges are amplified. A regional PV power prediction system is presented and evaluated by providing forecasts up to 72 h ahead with an hourly time resolution. The proposed approach is based on a local radiation forecast model developed by Blue Sky. In this paper, we propose a novel method of deriving forecast equations by using an irradiance classification approach to cluster the dataset. A separate equation is derived using the GEKKO optimization tool, and an algorithm is assigned for each cluster. Several other linear regressions, time series and machine learning (ML) models are applied and compared. A feature selection process is used to select the most important weather parameters for solar power generation. Finally, considering the prediction errors in each cluster, a weighted average and an average ensemble model are also developed. The focus of this paper is the comparison of the capability and performance of statistical and ML methods for producing a reliable hourly day-ahead forecast of PV power by applying different skill scores. The proposed models are evaluated, results are compared for different models and the probabilistic time series forecast is presented. Results show that the irradiance classification approach reduces the forecasting error by a considerable margin, and the proposed GEKKO optimized model outperforms other machine learning and ensemble models. These findings also emphasize the potential of ML-based methods, which perform better in low-power and high-cloud conditions, as well as the need to build an ensemble or hybrid model based on different ML algorithms to achieve improved projections.

Optimization of High Frequency Welding Parameters of PVC Coating on Polyester Fabric

This study optimized welding parameters on polyester fabric coated with PVC by applying high frequency welding technique. Taguchi experimental design was adopted in this evaluation. Three levels of width of seam and time of high frequency welding were applied and analyzed with ANOVA statistical method with α = 0.05. The optimized result shows that the input parameters providing maximum failing load for welding in warp and weft directions are the following: Width of seam (27 mm) and time of welding (12 s). The maximum failing load were 87.8 and 75.8 N/mm for warp direction and weft direction, respectively, In addition, the statistic analytical result indicated that the width of seam was the most significant factor and the predicted optimal value of failing load were 88.5 and 80.5 N/mm for warp direction and weft direction, respectively, shows that the average percentage error is less than 2 % so that the forecast equation is appropriate for the explanation of the variables for this study and provide statistical reliability. The result was verified through confirmation experiments, the high frequency welding was suitable for welding PVC Coating on Polyester Fabric.

Effects of planting methods on the utilization of temperature and sunshine resources and yield of rice under cabbage/rape-paddy cropping system.

Light simplified cultivation and high quality rice are the main directions of rice production in China. Meteorological factors are the most important environmental factors affecting rice growth and yield. Few studies examined the relationship between rice yield and microclimate under different light simplified cultivation modes. To explore the relationship between rice yield and climatic factors (temperature, sunshine and water) at different growth stages of hybrid rice under different forecrops in southwest China, we carried out a split-plot design experiment in 2019 and 2020, with two forecrops of green cabbage and rape as the main plot, and three planting methods, direct-seeding, blanket-transplanting, and artificial transplanting as the subplots, taking Yixiangyou 2115 as the experimental variety. Results showed that compared with rape-paddy cropping system, cabbage-paddy cropping system significantly improved the accumulated temperature and precipitation production efficiency and consequently improved the effective panicles, setting rate, and 1000-grain weight. The yield was increased by 12.7% and 8.3% under cabbage-paddy and rape-paddy cropping system, respectively. Compared with manual transplanting, mechanical transplanting improved effective panicles, production efficiency of radiation, accumulated temperature and precipitation, and the radiation use efficiency of grain during the whole growth period. The mean yield was increased by 4.6% in 2019 and 2020. However, the above parameters of direct-seeding significantly decreased, but the yield decreased by 8.7%. Compared with 2019, mechanical transplanting and artificial transplanting were sown one month earlier in 2020 under the same stubble, which shortened growth period, reduced air temperature, and increased precipitation after flowering, leading to a significant decrease in effective accumulated temperature and light radiation; production efficiency of accumulated temperature, light energy, and precipitation; and utilization efficiency of light energy of grain, spikelets per panicle, setting rate, and 1000-grain weight. However, the yield was significantly reduced. Partial least squares regression analysis was used to establish the production forecast equation of standardized regression coefficients of meteorological factors. There was a positive correlation between rice yield and effective accumulated temperature and total radiation during the growth stage or the whole growth period. In addition, there was a significant negative correlation between rice yield and precipitation during the whole growth period. In conclusion, mechanical transplanting under cabbage-paddy cropping system was a rice planting method that optimised the seasonal sunshine and temperature resources in southwest China. The method facilitated the full utilization of temperature and sunshine resources, resulting in high yield. However, it was not advisable to sow or transplant too early.

Advantages For Portfolio Management Through Cost Scaling Equations for Offshore Wind Turbine Components

This paper presents the key concerns of offshore wind turbine Original Equipment Manufacturers’ (OEMs) Product Portfolio Management (PPM) functions for the predicted turbine growth. In addition, it provides possible improvements within the portfolio decision process through available cost forecast equations for turbines’ main components.

Analysis of a Cold Wave Weather Process of China from December 23rd to 26th, 2021

Cold wave weather process is a large-scale strong cold air activity process, will bring drastic cooling and wind, as well as rain and snow weather. This research mainly studies the evolution and cause of a cold wave weather process of China from December 23rd to 26th, 2021. The weather map from the National Climate Center and the geopotential height and sea level pressure reanalysis data from December 23rd to 26th, 2021 provided by National Centers for Environmental Prediction of United States are analyzed by the comparative analysis method and the high altitude situation forecast equation. As a result, this research found that this cold wave was considered a nationwide process. Besides, this cold wave process was mainly caused by the invasion of cold air from Siberia. In addition, under the joint action of the warm high pressure ridge in the Ural Mountains, the polar vortex split and presented a multi-polar distribution. One of them was located near West Siberia, which was conducive to the accumulation of cold air here. With the rotation of the transverse trough, cold air moved southward, and cold wave broke out.

A Coastal Flood Event Database for the Southeastern Georgia and Southeastern South Carolina Coast and the Operational Implementation of a Tide Forecast Tool

Coastal flooding occurs when saltwater inundates normally dry land and the resulting impacts can range from minor flooding of low-lying areas along the coast, to significant damage to property and structures. Previous research consistently suggests that if sea-level rise continues to increase along the East Coast of the United States, coastal flooding will occur more frequently. In order to document the history of coastal flooding along the southeastern Georgia and southeastern South Carolina coast, a coastal flood event database was created for National Ocean Service tide gauges located in Charleston Harbor, South Carolina and Fort Pulaski, Georgia. Trends from the data show that coastal flooding is occurring more frequently with time at both tide gauges, particularly over the last five to ten years. Because of the increased frequency and worsening impacts of tidal flooding, a tide forecast tool is implemented operationally in an effort to improve deterministic tide forecasts. This study extends the dataset used in the Charleston Harbor forecast tool, expands the tool to Fort Pulaski, and compares the synoptic category forecast equations to an all-inclusive equation that does not differentiate by synoptic category. Results show that there is virtually no difference in the forecast accuracy between the all-inclusive forecast equation and the specific forecast equations based on synoptic category. Furthermore, the all-inclusive forecast equation can be implemented operationally, will help improve deterministic tide forecasts, and will likely aid in the decision-making process for Coastal Flood Watches, Warnings, and Advisories issued by the National Weather Service office in Charleston, South Carolina.

Improvement of Offshore Wind Turbine Portfolio Decisions Through Cost Forecast Equations

In this article we examine the research question ‘How cost forecast equations for offshore wind turbine main components can improve the portfolio decisions from Product Portfolio Management (PPM)?” by an extensive literature review of product portfolio decisions within PPM in the wind industry. In addition, two key experts from a leading offshore wind turbine OEM have been interviewed to answer this question and the method of qualitative content analysis as outlined by Mayring has been used to analyze the interviews and interpret the findings. It was analyzed how portfolio decisions for future offshore wind turbines are made and how future turbine costs are estimated. Therefore, this paper provides the scientific foundation by presenting the status quo of how PPM makes portfolio decisions for offshore wind turbines and with it how the future cost of offshore wind turbines are estimated. Aiming to use the presented results in future research in order to create accurate cost forecast models for the turbine main components and with it the creation of an overall turbine CAPEX scaling model.

Improving the Accuracy of Subseasonal Forecasting of China Precipitation With a Machine Learning Approach

Precipitation change, which is closely related to drought and flood disasters in China, affects billions of people every year, and the demand for subseasonal forecasting of precipitation is even more urgent. Subseasonal forecasting, which is more difficult than weather forecasting, however, has remained as a blank area in meteorological service for a long period of time. To improve the accuracy of subseasonal forecasting of China precipitation, this work introduces the machine learning method proposed by Hwang et al. in 2019 to predict the precipitation in China 2–6 weeks in advance. The authors used a non-linear regression model called local linear regression together with multitask feature election (MultiLLR) model and chosen 21 meteorological elements as candidate predictors to integrate diverse meteorological observation data. This method automatically eliminates irrelevant predictors so as to establish the forecast equations using multitask feature selection process. The experiments demonstrate that the pressure and Madden–Julian Oscillation (MJO) are the most important physical factors. The average prediction skill is 0.11 during 2011–2016, and there are seasonal differences in forecasting skills, evidenced by higher forecast skills of winter and spring seasons than summer and autumn seasons. The proposed method can provide effective and indicative guidance for the subseasonal prediction of precipitation in China. By adding another three factors, Arctic Oscillation (AO) index, Western North Pacific Monsoon (WNPM) index and Western North Pacific Subtropical High (WNPSH) index into the MultiLLR model, the authors find that AO can improve the forecast skill of China precipitation to the maximum extent from 0.11 to 0.13, followed by WNPSH. Moreover, the ensemble skill of our model and CFSv2 is 0.16. This work shows that our subseasonal prediction of China precipitation should be benefited from the MultiLLR model.

Study of the Accuracy of Methane Concentration Forecasts in the Area of Longwall Outlet B-3 in Seam 407/1 in KWK “Borynia-Zofiówka” Ruch Zofiówka

Abstract In the presented publication, an ex post forecast of methane concentration at the airway outlet was carried out on the basis of equations previously developed and repeatedly tested by H. Badura and its errors were calculated. This forecast was considered as a reference level. Next, using the same forecast equations, the forecasting of methane concentration at the sensor location up to 10 m in front of the longwall face and at the longwall outlet was carried out, followed by the analysis of forecast errors. Since the measurement of methane concentrations in the locations under consideration differed, the results of forecasts also differed. Therefore, in order to assess the accuracy of forecasts, their absolute and relative errors were calculated and then compared with the forecast errors at the airway outlet. The analysis of errors showed that there are differences in forecast accuracy and it is advisable to develop new forecast equations mainly for the longwall outlet, but also for the location of methane concentration sensor installed in the airway up to 10 m in front of the longwall.