Seasonal Autoregressive Integrated Moving Average Research Articles

Research on E-Commerce Retail Demand Forecasting Based on SARIMA Model and K-means Clustering Algorithm

With the rapid development of e-commerce, precise demand forecasting and efficient inventory management have become essential for the success and profitability of retail businesses. This study focuses on demand forecasting for e-commerce retailers using the Seasonal Autoregressive Integrated Moving Average (SARIMA) model and the K-means clustering algorithm. The research utilizes a dataset containing 1996 time series of sales data from various products, merchants, and warehouses, aiming to predict demand changes for the next 15 days. The study initially evaluates three models—Linear Regression (LR), Autoregressive Integrated Moving Average (ARIMA), and SARIMA—by fitting them to historical sales data to forecast future demand. The SARIMA model is identified as the most effective through rigorous evaluation using 1-mWAPE (mean weighted absolute percentage error) and RMSE (root mean square error) metrics. To enhance homogeneity within demand categories, the K-means clustering method is applied to divide products into four distinct groups, further refining the forecasting process.The paper also addresses the challenge of integrating new sequences into the dataset by leveraging clustering results to classify sequences and using cosine similarity to identify analogous historical time series. These matched sequences serve as the basis for demand prediction using the established SARIMA model. The findings highlight the robustness of the SARIMA model in capturing trends and seasonality, providing a reliable framework for e-commerce demand forecasting that can significantly impact inventory strategies and operational efficiency.

Twitter's capacity to forecast tourism demand: the case of way of Saint James

PurposeRadical changes in consumer habits induced by the coronavirus disease (COVID-19) pandemic suggest that the usual demand forecasting techniques based on historical series are questionable. This is particularly true for hospitality demand, which has been dramatically affected by the pandemic. Accordingly, we investigate the suitability of tourists’ activity on Twitter as a predictor of hospitality demand in the Way of Saint James – an important pilgrimage tourism destination.Design/methodology/approachThis study compares the predictive performance of the seasonal autoregressive integrated moving average (SARIMA) time-series model with that of the SARIMA with an exogenous variables (SARIMAX) model to forecast hotel tourism demand. For this, 110,456 tweets posted on Twitter between January 2018 and September 2022 are used as exogenous variables.FindingsThe results confirm that the predictions of traditional time-series models for tourist demand can be significantly improved by including tourist activity on Twitter. Twitter data could be an effective tool for improving the forecasting accuracy of tourism demand in real-time, which has relevant implications for tourism management. This study also provides a better understanding of tourists’ digital footprints in pilgrimage tourism.Originality/valueThis study contributes to the scarce literature on the digitalisation of pilgrimage tourism and forecasting hotel demand using a new methodological framework based on Twitter user-generated content. This can enable hospitality industry practitioners to convert social media data into relevant information for hospitality management.

Integrating structure time series forecasting and multicriteria decision analysis for adaptive operational risk assessment: an empirical study using real-time data

AbstractThis study propose a framework for integrating Multi-Criteria Decision Analysis (MCDA) and Structure Time Series (STS) prediction for multivariate operational risk assessment often with highly dynamic risk determinants. In particular, by utilizing the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) as an MCDA method, the framework is able to prioritize risk determinants according to their inherent uncertainties’ impacts on their respective operational objectives, and by employing Seasonal Autoregressive Integrated Moving Average (SARIMA) as an STS technique, the framework emphasizes real-time knowledge utilization for iteratively reducing uncertainty. By integrating SARIMA and TOPSIS, the framework aims to construct a multivariate operational risk assessment profile that is prioritized and continuously updated by the latest data and knowledge. Based on the proposed framework, the study constructs a mathematical model coded in Python to perform an empirical assessment of 161 countries’ operational risk using real-time data from the Armed Conflict Location & Event Data Project’s. A comprehensive analysis of the model’s functionality, quality, and sensitivity based on the assessment result is provided. Conclusions and limitations are also discussed, highlighting the model’s theoretical novelty and practical implications.

Assessing Groundwater Sustainability in the Arabian Peninsula and Its Impact on Gravity Fields through Gravity Recovery and Climate Experiment Measurements

This study addresses the imperative to comprehend gravity shifts resulting from groundwater storage (GWS) variations in the Arabian Peninsula. Despite the critical importance of water resource sustainability and its relationship with gravity, limited research emphasizes the need for expanded exploration. The investigation explores the impact of GWS extraction on the gravity field, utilizing Gravity Recovery and Climate Experiment (GRACE) and Global Land Data Assimilation System (GLDAS) data in addition to validation using the WaterGAP Global Hydrology Model (WGHM). Spanning April 2002 to June 2023, this study predicts GWS trends over the next decade using the Seasonal Autoregressive Integrated Moving Average (SARIMA) model. The comprehensive time-series analysis reveals a significant GRACE-derived groundwater storage (GWS) trend of approximately −4.90 ± 0.32 mm/year during the study period. This trend has a notable impact on the gravity anomaly (GA) values, as observed through the decomposition analysis. The projected GWS indicates a depletion rate of 14.51 km3/year over the next decade. The correlation between GWS and GA is substantial at 0.80, while the GA and rainfall correlation is negligible due to low precipitation rates. Employing multiple linear regression explains 80.61% of the variance in gravity anomaly due to GWS, precipitation, and evapotranspiration. This study investigates climate change factors—precipitation, temperature, and evapotranspiration—providing a holistic understanding of the forces shaping GWS variations. Precipitation and evapotranspiration exhibit nearly equal values, limiting GWS replenishment opportunities. This research holds significance in studying extensive GWS withdrawal in the Arabian Peninsula, particularly concerning crust mass stability.

Analysis of Climatic Variables on the Water Resources of the Ouémé River Using the Seasonal ARIMA Approach

Analysis of Climatic Variables on the Water Resources of the Ouémé River Using the Seasonal ARIMA Approach

Automotive Kit Demand Forecasting Using Advanced Forecasting Models: A Data-Driven Approach for Optimal Demand Forecasting

This study addresses the major challenges of forecasting automotive kit items(parts of vehicles) by enhancing the delivery of the products and managing the inventory. The kit items vary as per customers and it is unique on its own, where the uniqueness determines the vehicle parts. Customers are the major role players who provide the business hence, this study highlights various factors contributing to the customer’s choice of kit items with features consisting of vehicle name, original equipment manufacturer (OEM), Item Description (collection of vehicle parts) type of product (brand of vehicle) and monthly allotment of each kit item as per customer starting from 2021 April to 2024 January. We conducted an extensive analysis to assess a range of time series analysis techniques for predicting kit demand within the automotive industry, the methods we investigated encompassed Autoregressive (AR), Autoregressive Moving Average (ARMA) ,Autoregressive Integrated Moving Average (ARIMA), Seasonal Autoregressive Integrated Moving Average (SARIMA), Simple Exponential Smoothing (SES), Holt's Linear Trend Method - Double Exponential Smoothing, Triple Exponential Smoothing - Holt Winters, Long Short-Term Memory (LSTM) and advanced forecasting models such as prophet in evaluating the accuracy of these models, we employed key metrics such as Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE), this study aims to drive significant progress in the automotive industry by optimising inventory management reducing storage costs and improving delivery efficiency to ensure smooth business operations moreover the integration of visually engaging dashboards for real-time analysis of projected values plays a pivotal role in identifying crucial monthly demand trends this integration not only enhances operational efficiency but also fosters enriched customer engagement thereby facilitating sustained advancement within the automotive sector.

Epidemiology of respiratory syncytial virus in a large pediatric hospital in Central Italy and development of a forecasting model to predict the seasonal peak.

Respiratory Syncytial Virus (RSV) is responsible for the majority of acute lower respiratory infections in infants and can affect also older age groups. Restrictions linked to the emergence of the SARS-CoV-2 pandemic and their subsequent lifting caused a change in the dynamics of RSV circulation. It is therefore fundamental to monitor RSV seasonal trends and to be able to predict its seasonal peak to be prepared to the next RSV epidemics. We performed a retrospective descriptive study on laboratory-confirmed RSV infections from Bambino Gesù Children's Hospital in Rome from 1st January 2018 to 31st December 2022. Data on RSV-positive respiratory samples (n = 3,536) and RSV-confirmed hospitalizations (n = 1,895) on patients aged 0-18 years were analyzed. In addition to this, a SARIMA (Seasonal AutoRegressive Integrated Moving Average) forecasting model was developed to predict the next peak of RSV. Findings show that, after the 2020 SARS-CoV-2 pandemic season, where RSV circulation was almost absent, RSV infections presented with an increased and anticipated peak compared to pre-pandemic seasons. While mostly targeting infants below 1 year of age, there was a proportional increase in RSV infections and hospitalizations in older age groups in the post-pandemic period. A forecasting model built using RSV weekly data from 2018 to 2022 predicted the RSV peaks of 2023, showing a reasonable level of accuracy (MAPE 33%). Additional analysis indicated that the peak of RSV cases is expected to be reached after 4-5 weeks from case doubling. Our study provides epidemiological evidence on the dynamics of RSV circulation before and after the COVID-19 pandemic. Our findings highlight the potential of combining surveillance and forecasting to promote preparedness for the next RSV epidemics.

Empirical Approach to Modelling and Forecasting Inflation using ARIMA Model: Evidence from Tanzania

This study investigates the dynamics of the inflation rate (INFL) in Tanzania spanning from 1990 to 2021 using advanced time series analysis techniques. The dataset sourced from the World Bank online database serves as the basis for analytical exploration. Initially, the Autoregressive Integrated Moving Average (ARIMA) model is applied to understand underlying patterns in the INFL data. Transitioning to Seasonal ARIMA (SARIMA) modeling encounters challenges due to the absence of pronounced seasonality or clear trends. Unit root tests, including the Augmented Dickey-Fuller (ADF) and Phillips-Perron (PP) tests, assess the stationarity of the data. Following the identification of non-stationarity, differencing is employed to achieve stationarity. Estimation of ARIMA models (ARIMA (1,1,1) and ARIMA (2,1,1)) is conducted, with diagnostic checks confirming the suitability of the ARIMA (1,1,1) model. The study contributes to the understanding of inflation dynamics and facilitates evidence-based economic policymaking in Tanzania.

Empowering Sustainability: A Consumer-Centric Analysis Based on Advanced Electricity Consumption Predictions

This study addresses the critical challenge of accurately forecasting electricity consumption by utilizing Exponential Smoothing and Seasonal Autoregressive Integrated Moving Average (SARIMA) models. The research aims to enhance the precision of forecasting in the dynamic energy landscape and reveals promising outcomes by employing a robust methodology involving model application to a large amount of consumption data. Exponential Smoothing demonstrates accurate predictions, as evidenced by a low Sum of Squared Errors (SSE) of 0.469. SARIMA, with its seasonal ARIMA structure, outperforms Exponential Smoothing, achieving lower Mean Absolute Percentage Error (MAPE) values on both training (2.21%) and test (2.44%) datasets. This study recommends the adoption of SARIMA models, supported by lower MAPE values, to influence technology adoption and future-proof decision-making. This study highlights the societal implications of informed energy planning, including enhanced sustainability, cost savings, and improved resource allocation for communities and industries. The synthesis of model analysis, technological integration, and consumer-centric approaches marks a significant stride toward a resilient and efficient energy ecosystem. Decision-makers, stakeholders, and researchers may leverage findings for sustainable, adaptive, and consumer-centric energy planning, positioning the sector to address evolving challenges effectively and empowering consumers while maintaining energy efficiency.

The impact of Russo-Ukrainian war, COVID-19, and oil prices on global food security

ContextLight of recent global upheavals, including volatile oil prices, the Russo-Ukrainian conflict, and the COVID-19 pandemic this study delves into their profound impact on the import and export dynamics of global foodstuffs. With rising staple food prices reminiscent of the 2010–2011 global food crisis, understanding these shifts comprehensively is imperative. ObjectiveOur objective is to evaluate this impact by examining six independent variables (year, month, Brent crude oil, COVID-19, the Russo-Ukrainian conflict) alongside six food indicators as dependent variables. Employing Pearson's correlation, linear regression, and seasonal autoregressive integrated moving averages (SARIMA), we scrutinize intricate relationships among these variables. Results and conclusionsOur findings reveal varying degrees of association, notably highlighting a robust correlation between Brent crude oil and food indicators. Linear regression analysis suggests a positive influence of the Russo-Ukrainian conflict, Brent oil on food price indices, and COVID-19. Furthermore, integrating SARIMA enhances predictive accuracy, offering insights into future projections. SignificanceFinally, this research has a significant role in providing a valuable analysis into the intricate dynamics of global food pricing, informing decision-making amidst global challenges and bridging critical gaps in prior research on forecasting food price indices.

Market Dynamics: Enhancing Supply Chain Efficiency Through Demand Prediction

This research gives a state-of-the-art solution for web-based demand forecasting. Machine learning methods are employed to build the approach, which takes use of Seasonal Autoregressive Integrated Moving Average (SARIMA) modeling. Customers may utilize our system to provide product codes combined with the period's start and end dates in order to correctly anticipate future demand patterns. Through the combination of previous sales data with current market circumstances, competitive plans, and other external variables, our system is able to discover complicated consumer patterns. This gives data that may be utilized for inventory management, pricing strategies, and production scheduling. All things considered, the holistic plan optimizes resource usage, eliminates waste, and supports consumer satisfaction. A data-driven solution that takes advantage of AI capabilities is being offered as part of this joint endeavor. This solution is positioned to assist numerous sectors, including manufacturing, retail, and healthcare, and it will boost operational efficiency and sustainable development in a global economic framework. Keywords— Product Demand Forecasting, Inventory Management, Production Planning, Time Series Analysis, Predictive Analytics, Inventory Optimization, Demand Prediction System, Real-time Forecasting, Data-driven Decision Making, Supply Chain Optimization, Price Elasticity Analysis, Demand Volatility Management, Retail Analytics.

Machine Learning-Based Predictive Analysis on Electronic Billing Machines to Value Added Tax Revenues Growth

This paper explores the application of predictive analysis in assessing the impact of Electronic Billing Machines (EBMs) on Value Added Tax (VAT) revenue growth in Rwanda. EBMs represent a significant innovation in VAT revenue collection, yet negative taxpayer perceptions and a lack of understanding impede their full potential. Without predictive analysis, accurately projecting the growth in VAT revenues due to EBM implementation remains a challenge for the Rwanda Revenue Authority. Using machine learning techniques and historical data obtained from Rwanda Revenue Authority Annual reports, this research aims to develop a predictive system capable of forecasting the influence of EBMs on VAT revenue growth. Employing a descriptive survey design approach, secondary data analysis was conducted to gather information on VAT taxpayers, EBM users, VAT revenues, and other relevant variables. The study reveals a notable increase in EBM adoption among VAT payers over a six-year period, indicating successful governmental efforts. Time series analysis demonstrates a positive correlation between EBM usage and significant increases in both VAT and total tax revenues. The Seasonal Autoregressive Integrated Moving Average (SARIMA) model, specifically an ARIMA (1, 0, 0) model, is identified as suitable for forecasting VAT revenue growth due to its balance between accuracy and simplicity. The developed predictive system provides highly accurate forecasts of VAT revenue growth, facilitating informed fiscal policymaking and enhancing financial stability and revenue collection in Rwanda.

A novel time-varying grey Fourier model for variable amplitude seasonal fluctuation sequences

PurposeA time-varying grey Fourier model (TVGFM(1,1,N)) is proposed for the simulation of variable amplitude seasonal fluctuation time series, as the performance of traditional grey models can't catch the time-varying trend well.Design/methodology/approachThe proposed model couples Fourier series and linear time-varying terms as the grey action, to describe the characteristics of variable amplitude and seasonality. The truncated Fourier order N is preselected from the alternative order set by Nyquist-Shannon sampling theorem and the principle of simplicity, then the optimal Fourier order is determined by hold-out method to improve the robustness of the proposed model. Initial value correction and the multiple transformation are also studied to improve the precision.FindingsThe new model has a broader applicability range as a result of the new grey action, attaining higher fitting and forecasting accuracy. The numerical experiment of a generated monthly time series indicates the proposed model can accurately fit the variable amplitude seasonal sequence, in which the mean absolute percentage error (MAPE) is only 0.01%, and the complex simulations based on Monte-Carlo method testify the validity of the proposed model. The results of monthly electricity consumption in China's primary industry, demonstrate the proposed model catches the time-varying trend and has good performances, where MAPEF and MAPET are below 5%. Moreover, the proposed TVGFM(1,1,N) model is superior to the benchmark models, grey polynomial model (GMP(1,1,N)), grey Fourier model (GFM(1,1,N)), seasonal grey model (SGM(1,1)), seasonal ARIMA model seasonal autoregressive integrated moving average model (SARIMA) and support vector regression (SVR).Originality/valueThe parameter estimates and forecasting of the new proposed TVGFM are studied, and the good fitting and forecasting accuracy of time-varying amplitude seasonal fluctuation series are testified by numerical simulations and a case study.

Agricultural price prediction based on data mining and attention-based gated recurrent unit: a case study on China’s hog

Under the influence of the coronavirus disease and other factors, agricultural product prices show non-stationary and non-linear characteristics, making it increasingly difficult to forecast accurately. This paper proposes an innovative combinatorial model for Chinese hog price forecasting. First, the price is decomposed using the Seasonal and Trend decomposition using the Loess (STL) model. Next, the decomposed data are trained with the Long Short-term Memory (LSTM) and Seasonal Autoregressive Integrated Moving Average (SARIMA) models. Finally, the prepared data and the multivariate influence factors after Factor analysis are predicted using the gated recurrent neural network and attention mechanisms (AttGRU) to obtain the final prediction values. Compared with other models, the STL-FA-AttGRU model produced the lowest errors and achieved more accurate forecasts of hog prices. Therefore, the model proposed in this paper has the potential for other price forecasting, contributing to the development of precision and sustainable agriculture.

Comparison of SARIMA, Bagging Exponential Smoothing with STL Decomposition and Robust STL Decomposition for Forecasting Red Chili Production

Time series analysis enables the identification of trends and patterns in data, allowing for the development of forecasting models that predict future values. One effective approach for forecasting seasonal time series data is the Seasonal Autoregressive Integrated Moving Average (SARIMA) method. Bagging Exponential Smoothing with STL Decomposition (BES-STL) is an ensemble machine learning method aimed at enhancing forecasting accuracy. STL Method, which stands for Seasonal-Trend decomposition using Loess, is utilized to decompose time series data into three components, namely trend, seasonal, and remainder components. In the remainder component, the process of bootstrap aggregation (bagging) with Moving Block Bootstrapping (MBB) is used to obtain synthetic data, followed by averaging the value by month from the entire series as the forecast results. A comparative analysis was conducted using the SARIMA, BES-STL, and BES-RSTL models. The optimal model, with the lowest MAPE and RMSE, is then implemented to predict national red chili production. The results indicate that the SARIMA(1,1,1)(0,1,1)12 model has the best performance with a MAPE of 7.06 and a RMSE of 95,473. The top-performing model is utilized to forecast data from January to December 2022. Additionally, the forecasted results are compared to the actual data, resulting in a highly precise MAPE of 5.39.

Power consumption prediction for electric vehicle charging stations and forecasting income

Electric vehicles (EVs) are the future of the automobile industry, as they produce zero emissions and address environmental and health concerns caused by traditional fuel-poared vehicles. As more people shift towards EVs, the demand for power consumption forecasting is increasing to manage the charging stations effectively. Predicting power consumption can help optimize operations, prevent grid overloading, and power outages, and assist companies in estimating the number of charging stations required to meet demand. The paper uses three time series models to predict the electricity demand for charging stations, and the SARIMA (Seasonal Auto Regressive Integrated Moving Average) model outperforms the ARMA (Auto Regressive Moving Average) and ARIMA (Auto Regressive Integrated Moving Average) models, with the least RMSE (Root Mean Squared Error), MAE (Mean Absolute Error) and MAPE (Mean Absolute Percentage Error) scores in forecasting power demand and revenue. The data used for validation consists of charging activities over a four-year period from public charging outlets in Colorado, six months of charging data from ChargeMOD's public charging terminals in Kerala, India. Power usage is also forecasted based on wheels of vehicles, and finally, a plan subscription data from the same source is utilized to anticipate income, that helps companies develop pricing strategies to maximize profits while remaining competitive. Utility firms and charging networks may use accurate power consumption forecasts for a variety of purposes, such as power scheduling and determining the expected energy requirements for charging stations. Ultimately, precise power consumption forecasting can assist in the effective planning and design of EV charging infrastructure. The main aim of this study is to create a good time series model which can estimate the electric vehicle charging stations usage of power and verify if the firm has a good income along with some accuracy measures. The results show that SARIMA model plays a vital role in providing us with accurate information. According to the data and study here, four wheelers use more power than two and three wheelers. Also, DC charging facility uses more electricity than AC charging stations. These results can be used to determine the cost to operate the EVs and its subscriptions.

Loss-of-Life Analyses Based on Modified Arrhenius and Relative Aging Rate for Non-Thermally Upgraded Paper in Oil-Immersed Transformer

This study evaluates the Loss-of-Life (LOL) based on the modified relative aging rate of an Oil Natural Air Natural (ONAN) transformer with voltage and power ratings of 132/33 kV and 60 MVA. The study’s methodology included the determination of the Hotspot Temperature (HST) based on the differential equation in IEC 60076-7. The loading and ambient temperature profiles for HST determination are forecasted based on the Seasonal Autoregressive Integrated Moving Average (SARIMA). Next, a new relative aging rate was developed based on the Arrhenius equation, considering the pre-exponential factors governed by oxygen, moisture in paper, and acids at different content levels. The LOL was computed based on the new relative aging rate. The study’s main aim is to examine the impact of pre-exponential factors on the LOL based on modified Arrhenius and relative aging rate. The results indicate that the LOLs for different conditions increase as the oxygen, moisture, low molecular weight acid (LMA), and high molecular weight acid (HMA) increase. The LOLs are 46 days, 1,354 days, and 2,662 days in the presence of 12,000 ppm, 21,000 ppm, and 30,000 ppm of oxygen. In 1%, 3%, and 5% moisture, the LOLs are 477 days, 2,799 days, and 7,315 days. At 1% moisture, the LOL is 1,418 days for LMA, while for HMA, it is 122 days. The LMA has the highest impact on the LOL compared to other aging acceleration factors.

Web search volume as a near-real-time complementary surveillance tool of tick-borne encephalitis (TBE) in Italy

The Internet is an important gateway for accessing health-related information, and data generated through web queries have been increasingly used as a complementary source for monitoring and forecasting of infectious diseases and they may partially address the issue of underreporting. In this study, we assessed whether tick-borne encephalitis (TBE)-related Internet search volume may be useful as a complementary tool for TBE surveillance in Italy. Monthly Google Trends (GT) data for TBE-related information were extracted for the period between January 2017 and September 2022, corresponding to the available time series of TBE notifications in Italy. Time series modeling was performed by applying seasonal autoregressive integrated moving average (SARIMA) models with or without GT data. The search terms relative to tick bites reflected best the observed temporal distribution of TBE cases, showing a correlation coefficient of 0.81 (95 % CI: 0.71–0.88). Particularly, both the reported number of TBE cases and GT searches occurred mainly during the summer. The peak of disease notifications coincided with that of Google searches in 4 of 6 years. Once calibrated, SARIMA models with or without GT data were applied to a validation set. Retrospective forecast made by the model with GT data was associated with a lower prediction error and accurately predicted the peak timing. By contrast, the traditional SARIMA model underestimated the actual number of TBE notifications by 65 %. Timeliness, easy availability, low cost and transparency make monitoring of the TBE-related Internet search queries a promising addition to the traditional methods of TBE surveillance in Italy.

Does future tuna landing stock meet the target? Forecasting tuna landing in Malaysia using seasonal ARIMA model

PurposeThis study was conducted to forecast the monthly number of tuna landings between 2023 and 2030 and determine whether the estimated number meets the government’s target.Design/methodology/approachThe ARIMA and seasonal ARIMA (SARIMA) models were employed for time series forecasting of tuna landings from the Malaysian Department of Fisheries. The best ARIMA (p, d, q) and SARIMA(p, d, q) (P, D, Q)12 model for forecasting were determined based on model identification, estimation and diagnostics.FindingsSARIMA(1, 0, 1) (1, 1, 0)12 was found to be the best model for forecasting tuna landings in Malaysia. The result showed that the fluctuation of monthly tuna landings between 2023 and 2030, however, did not achieve the target.Research limitations/implicationsThis study provides preliminary ideas and insight into whether the government’s target for fish landing stocks can be met. Impactful results may guide the government in the future as it plans to improve the insufficient supply of tuna.Practical implicationsThe outcome of this study could raise awareness among the government and industry about how to improve efficient strategies. It is to ensure the future tuna landing meets the targets, including increasing private investment, improving human capital in catch and processing, and strengthening the system and technology development in the tuna industry.Originality/valueThis paper is important to predict the trend of monthly tuna landing stock in the next eight years, from 2023 to 2030, and whether it can achieve the government’s target of 150,000 metric tonnes.

Modelling and optimizing microgrid systems with the utilization of real-time residential data: a case study for Palapye, Botswana

Microgrids are becoming a realistic choice for residential buildings due to the increasing need for affordable and sustainable energy solutions in developing nations. Through modeling and simulation, the main goal is to evaluate the viability and performance of a solar microgrid system. Residential load modeling is used, which is vital to developing an effective Energy Management System (EMS) for the microgrid. A residential household’s load metering data is examined using statistical methods, including time series and regression analysis. For the residential community load in this research, Proportional-Integral-Derivative (PID) controllers and Fuzzy Logic Controllers (FLC) are used to generate the necessary Direct Current (DC) microgrid voltage. The simulation research shows that FLC have benefits over PID controllers. The FLC technique performs better at reducing total harmonic distortion, which improves the microgrid system’s overall power quality. The Seasonal Autoregressive Integrated Moving Average (SARIMA) model was found to be the most appropriate and reliable model for the dataset after the performance of the models was evaluated using the metrics. The optimization results also showed that FLC optimization improves the microgrid system’s stability. The exponential Gaussian process regression (GPR) produced the highest R-squared measure of 0.49 and RSME measure of 7.9646, making it the best goodness fit for modeling the total daily energy usage and the peak daily usage.