Forecast Values Research Articles

Application of ARIMA Methods on Unemployment and Inflation Rates in Nigeria

In Nigeria, Unemployment and inflation rate are some of the problems bedeviling the economy. The inability of Job seekers to secure gainful employment tends to create disaffection among these people and cause some of them especially the youth, to resort to social vices. In the motivation to experience low unemployment rate and inflation rate which are economic indicators in Nigeria, deriving appropriate ARIMA model to give insight into future occurrence of these indicators for intervention calls for the application of ARIMA model. This study seek to fit ARIMA model to unemployment rate and inflation rate and forecast future values for both unemployment rate and inflation rate. Utilizing secondary data sourced from the National Bureau of Statistics (NBS) from 1991 to 2020. The findings reveal that, for each of the two series, to obtain the appropriate ARIMA model that fits the data, ten closely suitable ARIMA models were identified. Using the least values of AIC, AICc and BIC as selection criteria, ARIMA (0, 1, 1) was found to be the best fitted model for each of the two series. Subsequent diagnostic checks confirm the adequacy of the fitted models, with residual analyses indicating no significant autocorrelation or heteroscedasticity. The forecasted values for the unemployment rate series and inflation rate series maintain a constant point forecast. This implies that unemployment rate and inflation rate in Nigeria does not reveal a noticeable increase nor decrease but will be constant in Nigeria. Due to the unchanging forecasted value which appears not to be decreasing, there is need for interventions that will lead to the visible reduction of present unemployment rate and inflation rate in Nigeria.

Two‐stage stochastic energy and reserve market clearing model considering real‐time offering strategy of renewable energy

AbstractWith the introduction of decarbonization policies worldwide, renewable energy, especially wind and solar power, is gradually taking a significant share in the power system. The two‐stage stochastic optimization methods are generally adopted to address the uncertainty issues and the two‐stage stochastic market clearing models are established with the participation of renewable energy. However, most existing two‐stage stochastic market clearing models usually intuitively presume the offering quantities of renewable energy are equal to their forecast values, neglecting the potential impacts of the strategic offerings on the market results and thus affecting the secure operation of power systems. To fill the gaps, this paper incorporates the strategic offering models of renewable energy into the two‐stage stochastic energy and reserve market clearing model. Within the two‐stage framework, the first day‐ahead stage serves for energy and reserve market clearing, while the second real‐time stage considers the adjustments on offering quantities of renewable energy according to the fluctuations of renewable generations. The two‐stage stochastic market clearing model is formulated as a mixed‐integer non‐linear programming problem, and an iterative algorithm is devised to obtain market clearing results after proper linearizations. The effectiveness of the proposed method is validated using the extended IEEE‐118 test system.

Repeated time-series cross-validation: A new method to improved COVID-19 forecast accuracy in Malaysia

Forecasting COVID-19 cases is challenging, and inaccurate forecast values will lead to poor decision-making by the authorities. Conversely, accurate forecasts aid Malaysian government authorities and agencies (National Security Council, Ministry of Health, Ministry of Finance, Ministry of Education, and Ministry of International Trade and Industry) and financial institutions in formulating action plans, regulations, and legal acts to control COVID-19 spread in the country. Therefore, this study proposes Repeated Time-Series Cross-Validation, a new data-splitting strategy to identify the best forecasting model that is capable of producing the lowest error measures value and a high percentage of forecast accuracy for COVID-19 prediction in Malaysia. Some of the highlights of the proposed method are:•A total of 21 models, five data partitioning sets, and four error measures to improve the forecast accuracy of daily COVID-19 cases in Malaysia.•The best model selected produces the lowest error measure value for the Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Mean Absolute Scaled Error (MASE).•The average 8-day forecast accuracy is 90.2 %. The lowest and highest forecast accuracy was 83.7 % and 98.7 %.

Ridge estimation for uncertain autoregressive moving average model with imprecise observations

An uncertain time series is a sequence of imprecisely observed values arranged in chronological order, whose main purpose is to predict future values based on previously observed values. It is significant to select an appropriate parameter estimation method in uncertain time series analysis. Firstly, the paper transforms a one-order uncertain autoregressive moving average model into an uncertain autoregressive model by the iterative method. Secondly, the ridge method is used to estimate the unknown parameters in the uncertain autoregressive moving average model, in which the shrinkage parameter is determined by ridge trace analysis. Thirdly, the forecast value and confidence interval are acquired by the residual analysis of the fitted model. Finally, two examples are provided to verify the validity and feasibility of the method. The result shows that the ridge method can effectually cut down the affection of outliers and improve the prediction accuracy compared with the least square estimation.

Cognitive Model Based on The Dl Random Forest Method for Profi t Forecasting and Fuzzy Algorithm for Assessing Company Sustainability in Conditions of Uncertainty

The article is devoted to the problem of supporting management decision-making on choosing a strategic partner whose activities would be effective and sustainable. During the study, based on the results of the work of enterprises in the domestic confectionery industry, a dataset was generated, which was subsequently used for the deep learning model DL-model "Random Forest" in order to calculate the predicted values of the net profi t of enterprises in the industry. Assessing companies for the purpose of selecting a strategic partner, using models such as: deep learning model "Random Forest" (DL Random Forest), VaR, Z-Altman, Hurwitz matrix, Fuzzy algorithm in modern conditions has great practical significance. The relevance of the study lies in the fact that in conditions of increasing market uncertainty, approaches to ensuring the sustainable development of an organization based on AI systems are increasingly being used. The scientific novelty lies in the fact that the study used a set of models that made it possible to assess the actual sustainability of companies and, based on the calculated forecast values of net profi t, as well as business efficiency indicators ROE and ROS, to make a decision on the choice of potential business partners. During the study the following were developed: a VaR model, which made it possible to obtain an assessment of fi nancial risk, a Z-Altman model for assessing the risk of bankruptcy of an enterprise. In addition, based on the calculated parameters of the regression equation, a Hurwitz matrix was formed, which made it possible to draw a conclusion about the sustainability of each enterprise as a system. The use of the Fuzzy algorithm made it possible to obtain a decision on choosing a partner enterprise.

Economic Benefits and Uses of Indigenous Seasonal Weather Forecasts in Zimbabwe

This study presents a seminal contribution regarding the economic value of indigenous seasonal weather forecasts in Zimbabwe. Many farmers (58%) use indigenous seasonal weather forecasts to make maize farming decisions such as selecting suitable varieties. The main indicators used for indigenous seasonal weather forecasts are flowering and fruition of specific trees. Based on travel cost analysis, which incorporates a multi-purpose visit scenario, the study establishes the economic importance of indigenous seasonal weather forecasts with a consumer surplus of US$1,044 per year among the 290 farmers using the forecasts. There is therefore a need to integrate indigenous weather forecasts into national seasonal weather forecasting and disaster risk reduction systems to complement modern seasonal weather forecasts. Co-production of seasonal weather forecasts with farmers is proposed in this regard. This further calls for the need to digitally document, visualize, and disseminate indigenous seasonal weather forecast indicators to a wider audience to increase their use.

Современные угрозы и обоснование изменений в регулировании экономической безопасности региона

The article considers the main approaches of the theory of economic security. The concepts of "ensuring economic security", "managing economic security", "regulating the economic security of the region" are studied. The impacts affecting the adaptive readiness of the regional economic security complex, reflected in the concepts of "threat", "risk", "factor", "challenge" are noted. The expediency of a multi-vector approach to the organization of regulatory impacts of state and municipal government bodies aimed at ensuring the economic security of the country's regions in the context of overcoming modern threats is determined. A thesis is formulated about the place of regulation of regional economic security as an integral link between the systemic federal level of national security and heterogeneous regions of the country. Using the example of Perm Krai, the dynamics of the main indicators of regional economic security and their relationship with similar indicators of the Volga Federal District and the Russian Federation are considered, and an assessment of existing trends is carried out. The analysis of the elaboration of regulatory and legal documents of the Perm Territory using the extrapolation method of processing official statistics information has established the insufficiency of the measures taken to improve strategic planning in the region in the context of taking into account changing factors and conditions of the external environment. Significant deviations of the achieved results in ensuring the economic security of the Perm Territory from the forecast values approved by the strategic planning documents have been revealed, in particular in achieving the planned indicators in the areas of industrial and environmental safety of the region. It is proposed to revise the content of regulatory documents with the corresponding budget and resource content for the timely adaptation of the region to modern threats and challenges. It is recommended to pay special attention to such areas of economic security of the Perm Territory as: technological sovereignty, investment security and environmental protection.

Study of the driving factors of the abnormal influenza A (H3N2) epidemic in 2022 and early predictions in Xiamen, China

BackgroundInfluenza outbreaks have occurred frequently these years, especially in the summer of 2022 when the number of influenza cases in southern provinces of China increased abnormally. However, the exact evidence of the driving factors involved in the prodrome period is unclear, posing great difficulties for early and accurate prediction in practical work.MethodsIn order to avoid the serious interference of strict prevention and control measures on the analysis of influenza influencing factors during the COVID-19 epidemic period, only the impact of meteorological and air quality factors on influenza A (H3N2) in Xiamen during the non coronavirus disease 2019 (COVID-19) period (2013/01/01-202/01/24) was analyzed using the distribution lag non-linear model. Phylogenetic analysis of influenza A (H3N2) during 2013–2022 was also performed. Influenza A (H3N2) was predicted through a random forest and long short-term memory (RF-LSTM) model via actual and forecasted meteorological and influenza A (H3N2) values.ResultsTwenty nine thousand four hundred thirty five influenza cases were reported in 2022, accounting for 58.54% of the total cases during 2013–2022. A (H3N2) dominated the 2022 summer epidemic season, accounting for 95.60%. The influenza cases in the summer of 2022 accounted for 83.72% of the year and 49.02% of all influenza reported from 2013 to 2022. Among them, the A (H3N2) cases in the summer of 2022 accounted for 83.90% of all A (H3N2) reported from 2013 to 2022. Daily precipitation(20–50 mm), relative humidity (70–78%), low (≤ 3 h) and high (≥ 7 h) sunshine duration, air temperature (≤ 21 °C) and O3 concentration (≤ 30 µg/m3, > 85 µg/m3) had significant cumulative effects on influenza A (H3N2) during the non-COVID-19 period. The daily values of PRE, RHU, SSD, and TEM in the prodrome period of the abnormal influenza A (H3N2) epidemic (19–22 weeks) in the summer of 2022 were significantly different from the average values of the same period from 2013 to 2019 (P < 0.05). The minimum RHU value was 70.5%, the lowest TEM value was 16.0 °C, and there was no sunlight exposure for 9 consecutive days. The highest O3 concentration reached 164 µg/m3. The range of these factors were consistent with the risk factor range of A (H3N2). The common influenza A (H3N2) variant genotype in 2022 was 3 C.2a1b.2a.1a. It was more accurate to predict influenza A (H3N2) with meteorological forecast values than with actual values only.ConclusionThe extreme weather conditions of sustained low temperature and wet rain may have been important driving factors for the abnormal influenza A (H3N2) epidemic. A low vaccination rate, new mutated strains, and insufficient immune barriers formed by natural infections may have exacerbated this epidemic. Meteorological forecast values can aid in the early prediction of influenza outbreaks. This study can help relevant departments prepare for influenza outbreaks during extreme weather, provide a scientific basis for prevention strategies and risk warnings, better adapt to climate change, and improve public health.

Quantifying the value of probabilistic forecasts when trading renewable hybrid power parks in day-ahead markets: A Nordic case study

Renewable hybrid power parks (HPPs) that combine wind power, solar photovoltaic (PV) power and storage have emerged as promising electricity generation resources. However, HPPs face operational challenges due to the uncertainty in power production and electricity prices, which is why probabilistic forecasts that capture the uncertainty associated with forecast errors have gained attention. While the research community has proposed several methods to improve the accuracy of probabilistic forecasts, the question on how these forecasts can improve decision-making over deterministic forecasts is rarely quantified. This study assesses the value of probabilistic forecasts and analyze the improvement compared to deterministic forecasts in day-ahead markets. The value is quantified using almost two years of data from an operational HPP in Sweden. Results show that: (i) high grid connection capacities leverage the value of probabilistic models, (ii) a deterministic model is preferable for parks with a ratio of battery energy capacity to installed nominal power of the renewable power park equal to 0.6 MWh/MW, (iii) a probabilistic model allows utilizing the energy storage more effectively by reducing the energy throughput of the battery with 61%–87%, and (iv) a probabilistic model increases the unit profit when the forecast errors of the regulating price are higher than the spot price, (v) a simple probabilistic benchmark model, which is worse in terms of forecast accuracy, increases the unit profit compared to the analyzed deterministic models, and (vi) the more advanced probabilistic model analyzed in this study does not provide a significant improvement over a simple probabilistic benchmark model.

A predictive model for the estimation of industrial [formula omitted] emissions for IoT-based devices

The paper is devoted to solving the problem, associated with increasing the forecasting accuracy of pollutant concentration level based on PM2.5 dust. The LANN model (LANN – Lagrange powered Artificial Neuron Network), proposed in the paper, allows you to take into account the mutual influence of pollution sources on the concentration level at monitoring points, which, on the one hand, allows to manage the volume of emissions from industrial enterprises in such a way as to avoid penalties and, on the other hand, allows the regulatory agencies to determine points for environmental control, at which permissible standards will be most likely exceeded. The suggested solution makes it possible to use low-cost IoT-based air quality monitoring devices, which were primarily considered not applicable due to their low accuracy and exposure to the influence of short-term stochastic factors; besides, no models were available, which would be capable to provide the necessary accuracy and forecast horizon on the base of IoT data. At the same time, the obtained model has low requirements for computing resources compared to dispersion models and is dependent on the accuracy of weather information; besides, the model allows you to take into account trends and obtain better forecast values in terms of accuracy than the models, that use regression methods, neural network models and ML models, stochastic models and their ensembles. The application of the model improves well-known pollutant dispersion estimation calculation techniques through the use of measurement data flow and dynamic adaptation to changing conditions The model was trained on the dataset over the period 2022-2023 collected from 5 monitoring devices, which were installed in the catchment area of the group, incl. 13 industrial points of PM2.5 pollutant sources.

A hybrid deep learning-based short-term forecast model for ionospheric foF2 in East Asia region

The short-term forecast of the critical frequency of the ionospheric F2 layer (foF2) is particularly challenging due to its nonlinear and non-stationary characteristics. To improve the short-term forecast accuracy of foF2, we propose a short-term hybrid deep learning model that integrates the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) and long short-term memory (LSTM) algorithms. The proposed model leverages the ICEEMDAN decomposition algorithm to expand foF2 time series data into multi-dimensional space and utilizes the LSTM algorithm to preserve long-term data information, capturing detailed changes in foF2 parameters and obtaining internal information about foF2. The 2014 and 2017 foF2 data from four observation stations located in Mohe, Beijing, Jeju, and Kokubunji in the mid-latitudes of East Asia were utilized for modeling and 1-hour forecast analysis. The forecast values of the proposed model align closely with the measured data, exhibiting minimal fluctuation in forecast error and effectively tracking the changing trend of foF2. The average root mean square error (RMSE) across the four stations in 2014 is 0.40 MHz, with a relative RMSE (RRMSE) of 7.11 % and a coefficient of determination (R2) of 0.98. The average RMSE in 2017 is 0.43 MHz, RRMSE is 7.68 %, and R2 is 0.92. The proposed model reveals significant improvements in the forecast accuracy of the comparison model compared with the CCIR, URSI, NN, and LSTM models. Especially compared with the CCIR and URSI models in the International Reference Ionosphere (IRI), demonstrating superior real-time tracking capability for foF2 and effectively addressing the challenge of poor short-term forecast accuracy observed in the IRI model.

Methodological Tools for Assessing Employment and Income of Rural Population under Unpredictable Conditions

Introduction. The modern Russian economy is facing serious problems in the field of labor resources, which leads to high unemployment and low wages. The development of effective solutions requires conducting evidence-based programs, including actions at the enterprise level, the regional labor market and the national level. The options offered to date by various researchers for assessing the employment and income of the rural population are characterized by an aspect and a lack of an integrated approach. Insufficient attention is paid to the integration of estimates, as well as the development of forecast values based on the expected dynamics of stochastic factors. The purpose of the study is to develop a methodology for assessing the employment of the rural population in the production of beekeeping products. Methods. The state and dynamics of the beekeeping industry development was analyzed on the example of the Perm Region. The study of indicators for assessing the employment level of the rural population included the effect of stochastic factors. The uncertainty influence was accounted by the use of correlation and regression analysis and a multidimensional model. The development of a population assessment methodology was based on the method of mathematical modeling. Results. The influence percentage of stochastic factors on changes in the employment level of the economically active population and the complex impact indicator of factors on the of employment level and incomes of the rural population of the Perm Territory were calculated with a forecast for 5 years. Conclusions. The proposed approach will make it possible to more effectively monitor the trends and dynamics of beekeeping development, which will further help to develop effective support measures for this industry.

VINNYTSIA REGION: STATUS AND TRENDS OF PRODUCTION, PROCESSING AND STORAGE OF GRAIN

The article is devoted to the study of the state of production, processing and storage of grain in the Vinnytsia region. The objectives of the research were to monitor the collected areas and gross harvests of the main crops in 2011-2021, establish the trends of their changes, analyze the capacities of transport and technological equipment and granaries of the region. The analysis was conducted on the basis of data from the State Statistics Service of Ukraine for 2011-2021. The analysis of harvested areas and gross harvests of grain of the main crops at agricultural enterprises of the region in 2011–2021 showed their tendency to increase. During this period, harvested areas increased by 1.42 times, and annual volumes of gross grain harvests had a wave-like growth pattern. Linear trend equations are proposed for forecasting harvested areas and gross grain harvests, which are characterized by coefficients of determination R2 of 0.79 and 0.65, respectively. The largest gross harvests were given by corn, wheat and sunflower, and smaller – by barley, soybeans and rapeseed. In 2018-2021, the gross harvest of these crops was within the following limits (thousand tons): corn 2015-2620, wheat 1200-1691, and sunflower 755-1001. 66 enterprises receive grain from vehicles, and 65 forward it. The average capacity of receiving grain is 3032.41 t/day, and forwarding 1183.61 t/day. There are 21 railway transport enterprises hiring, and 55 enterprises offering leave. The average capacity of receiving devices from the railway is 949.52 t/day, and the output capacity is 1448.69 t/day. 111 grain dryers from 11 producing countries are installed at the considered 53 enterprises of the region. Of them, 18.9% of enterprises have dryers of Ukrainian production only, 20.8% of enterprises have both domestic and foreign ones, but the majority of enterprises have dryers of foreign production only, which are 1.85 times more than the number of Ukrainian made dryers. It was established that 82 separators are used for grain cleaning at 37 elevators, most of which (63%) are domestically produced, which outnumber imported ones by 1.73 times. The total capacity of simultaneous storage of grain in 67 elevators of agricultural enterprises is 3554.35 thousand tons, of which 2449.41 thousand tons have harvesting elevators and 1104.94 thousand tons – production elevators. Their simultaneous storage capacity is less than the forecast values of gross grain harvests in 2022 – 6354.2 thousand tons, which requires the expansion of the elevator network of the Vinnytsia region.

Development and implementation of effective Web Scraping methods for automated data collection and processing using Python

With each passing year, the process of digitalization in society is accelerating, leading to a significant increase in demand for processed and analyzed information. In today's world, data has become a valuable resource, and the ability to quickly find and analyze large amounts of information is a key competitive advantage for companies, researchers, and analysts. In this context, web scraping has become an important tool, enabling the efficient collection of data from various online sources for further analysis and informed decision-making. This paper examines the latest advancements in the development and implementation of effective web scraping methods for automatic data collection and processing using Python. The use of the latest Python libraries, such as BeautifulSoup, Selenium, and Scrapy, allows for high-speed and accurate data collection from various web sources, particularly in secondary markets. The proposed algorithms reduce the risk of site blocking, ensuring the stability and reliability of data collection in various situations. Additionally, the paper places great emphasis on automating the data collection process through the development of automated scripts and the implementation of job scheduling programs, such as cron jobs. This ensures continuous database updates and the collection of new information without manual intervention. Special attention is given to the processing and cleaning of collected data, particularly in the development of methods for filtering out unnecessary information, duplicates, and noise, which enhances data quality. The efficient use of the collected data demonstrates its value for market analysis, demand assessment, and quality forecasting, highlighting the importance of the proposed method. The research includes examples of real-world data use cases in various fields such as marketing, economics, and business analysis. A comparative analysis of different data collection methods is also provided, allowing for the assessment of the effectiveness and reliability of the proposed solutions.

Cluster Analysis and Forecasting on Local Shoe Products: Case study for Ventela in Indonesia

Shoes are a secondary need that is in demand by all age groups. Each shoe brand has many models, which means a store must provide complete stock to meet consumer needs. The available models have different purchasing power or demand, which creates difficulties for stores in determining shoe products that are often sold and shoe products that are not in demand by customers. The data used in cluster formation includes three variables recorded and collected from e-commerce transactions, namely Number of Visitors, Number of Buyers, and Total Sales. Based on these variables, Ventela shoe models are grouped into three clusters, namely low-selling, normal and best-selling. Next, the variable number of transactions for Ventela shoe models in the best-selling cluster is taken to be predicted using the Exponential Smoothing method. The forecasts obtained are used to determine future demand to maximize profits. Based on the results of the clustering analysis, it was found that the number of shoe models included in the best-selling cluster was six, including (1) Ventela Ethnic Low All Black, (2) Ventela Ethnic Low Black Natural, (3) Ventela Public Low Black Natural, (4) Ventela Public Low Cream, (5) Ventela Republic Low Black Natural, and (6) Ventela Republic Low White. Referring to the sample of this study, which only spanned less than three years, several shoe models produced a forecast value of zero. It means, based on the forecast results for the next 12 months, there may be no sales.

Trend analysis of cotton production and trade

The aim of this study is to analyse the cultivation areas, production amounts and foreign trade data of cotton plant in the world and Turkey from an economic point of view. The data on Turkey's cotton cultivation area, cotton production and fiber cotton import and export data for the 20-year period 2003-2022 were obtained from the Food Agricultural Organization (FAO). The data set was transferred to MS Excel spreadsheets and the trend analysis technique was used in MS Excel programme to obtain the trends in cotton cultivation area, cotton production amount and near future forecast values for eight years between 2023-2030. In addition, for the foreign trade balance data obtained from fiber cotton import and export data for the period 2000-2022, the change trends of the 23-year period and the eight-year near future forecast values between 2023-2030 were calculated. Cotton cultivation area has a decreasing trend between 2023-2030. While the forecast value of cotton cultivation area for 2023 is 417 thousand hectares, this value is predicted to decrease to 373 thousand 722 hectares in 2030. The trend in the amount of cotton production has a decreasing trend after 2022 and it is predicted that there will be no break in the amount of production between 2023-2030. The estimated foreign trade deficit in fiber cotton for 2023 is 964,516 tonnes and for 2030 is 1,106,672 tonnes. The main reasons for the decrease in cotton cultivation areas are the increases in input prices such as fertilisers, pesticides and seeds. In order to eliminate the foreign trade deficit in cotton, studies should be carried out to enter new markets and increase the share in existing markets. In addition, it should be aimed to create a sustainable cotton sector by investing in R&amp;amp;D activities and entering new markets.

Sustainable Textile Manufacturing with Revolutionizing Textile Dyeing: Deep Learning-Based, for Energy Efficiency and Environmental-Impact Reduction, Pioneering Green Practices for a Sustainable Future

The textile industry, a substantial component of the global economy, holds significant importance due to its environmental impacts. Particularly, the use of water and chemicals during dyeing processes raises concerns in the context of climate change and environmental sustainability. Hence, it is crucial from both environmental and economic standpoints for textile factories to adopt green industry standards, particularly in their dyeing operations. Adapting to the green industry aims to reduce water and energy consumption in textile dyeing processes, minimize waste, and decrease the carbon footprint. This approach has become crucial in achieving sustainability in textiles following the signing of the Paris Climate Agreement. Important elements of this transformation include the reuse of washing waters used in the dyeing process, the recycling of wastewater, and the enhancement of energy efficiency through necessary methodological and equipment changes. This study analyzes the energy, labor, production, and consumption data since 2011 for a textile factories with four branches located in the Adana Organized Industrial Zone. Among these factories, the one designated as UT1, which has the highest average energy and water consumption compared to the other three branches, is selected. In recent years, the use of artificial intelligence and machine learning technologies in predicting industrial processes has been increasingly observed. The data are analyzed using LSTM (Long Short-Term Memory) and ANN (Artificial Neural Networks) forecasting methods. Particularly, the LSTM algorithms, which provided the most accurate results, have enabled advanced forecasting of electricity consumption in dyeing processes for future years. In 2020, electricity consumption was recorded as 3,717,224 kWh and this consumption was reflected in the total energy cost as TRY 1,916,032. Electricity consumption accounts for 22.34% of total energy consumption, while the share of this energy type in the cost is 43.25%. In the light of these data, the MAPE value for energy consumption forecasts using the LSTM model was 0.45%, which shows that the model is able to forecast with high accuracy. As a result, a solar power plant was installed to optimize energy consumption, and in 2023 60% energy savings were achieved in summer and 25% in winter. The electricity consumption forecasting results have been an essential guide in planning strategic initiatives to enhance factory efficiency. Following improvement efforts aimed at reducing energy consumption and lowering the carbon footprint, significant optimizations in processes and layouts have been made at specific bottleneck points within the facility. These improvements have led to savings in labor, time, and space, and have reduced unit production costs.

Assessing the value of harmful algal bloom forecasts in the Pacific Northwest

Abstract Over the past three decades, fisheries and livelihoods on the coasts of Washington and Oregon have been severely impacted by the presence of harmful algal blooms (HABs) that produce domoic acid, a neurotoxin that accumulates in shellfish and endangers public health. Among others, recreational razor clams and commercial Dungeness crab fisheries along the Pacific Northwest (PNW) have been negatively affected, jeopardizing the economies of coastal communities that depend on tourist revenues and income generated through visits of harvesters in the region. The PNW HAB Bulletin, launched in 2008, publishes forecasts on incoming HAB events, which has enabled managers to increase toxin monitoring in high-risk locations and proceed with selected harvesting at safe beaches and delays or closures of fishing seasons, as required. In light of the value of the HAB Bulletin to local managers and communities and the occasional challenges of securing sufficient resources to ensure its continuation, this study attempts to assess the value of information (VOI) for the predictions provided by the Bulletin. Results of the study show that ongoing financial support of the Bulletin is economically justifiable. The value of HAB forecast is positively related to three primary factors: the frequency of HAB events, the precision of forecast, and the number of social and economic sectors benefiting from the forecast. The expected increase in HAB frequency and intensity due to climate change, coupled with advancements in forecasting accuracy through technological development, is anticipated to enhance the value of the forecast program.

Insights to key operational questions in forecast-informed dam release operation: case of Hume Dam

ABSTRACT Dam operators decide on dam releases given downstream demands and other factors. Downstream tributaries complicate the problem due to significant uncertainties in their flows. Demands can also be highly uncertain. Incorporating probabilistic ensemble forecasts into the decision-making process can mitigate these uncertainties. We investigate how ensemble flow and demand forecasts can inform Hume Dam releases, in the Murray-Darling Basin, for delivery to Lake Mulwala, about four days downstream. We develop and simulate three forecast-based decision rules governing the releases. The first and second rules are based on the expectations and medians respectively of ensemble forecasts of water excess and shortage, forced by ensemble tributary inflow and demand forecasts. The third rule is based on the risks of an excess or shortage per the ensemble forecasts of water excess and shortage. We analyse the results to answer the following key operational questions: (1) Which decision rule is best for managing Hume Dam releases for water supply and under what conditions? (2) How far ahead of flow and demand forecasts should Hume Dam operators adopt? (3) How does limiting the day-to-day change in Hume Dam releases influence the value of forecasts? (4) What is the value of improved demand forecasts?

ANALYSIS OF DETERMINANTS OF DEMAND FOR SPECIALIST POLYCLINIC SERVICES AT THE INDONESIAN RED CROSS HOSPITAL, NORTH ACEH

Demand Health is an individual's decision regarding choices in utilizing health services. Demand for health services provided in primary, secondary and tertiary health facilities, as well as including public health, varies. Utilization of health services at the Indonesian Red Cross Hospital in North Aceh, especially at specialist polyclinic services, appears to be still very low. This can be shown from the number of visits each month which fluctuates up and down with a trend line showing the forecasting value in a downward direction. This research aims to analyze the Determinants of Demand for Specialist Polyclinic Services at the Indonesian Red Cross Hospital, North Aceh. This type of research is descriptive analytical with a cross-sectional design with a quantitative approach. Data analysis was carried out through univariate and bivariate stages.There is no significant relationship between gender with P value = 0.701, age with P value = 0.383, education level with P value = 0.105, income with P value = 0.75, health insurance with P value = 0.511, waiting time with P value = 0.260, type of disease with P value = 0.895, severity of disease with P value = 0.385 and quality of service with P value = 0.369 for service demand at the specialist polyclinic of the Indonesian Red Cross Hospital, North Aceh. The conclusion from this research is that there are no variables that can significantly directly influence the demand for specialist polyclinic services at the Indonesian Red Cross Hospital in North Aceh, however, the researcher strongly recommends various efforts and initiatives from all levels, both management and implementing officers, to always improve the services provided. to patients in order to create high demand from patients who have visited and people who have never visited the PMI North Aceh hospital.