Forecasting Model Research Articles

Assessing the impact of the National Clean Air Programme in Uttar Pradesh's non-attainment cities: a prophet model time series analysis

BackgroundUttar Pradesh, India’s largest state, faces critical pollution levels, necessitating urgent action. The National Clean Air Programme (NCAP) targets a 40% reduction in particulate pollution by 2026. This study assesses the NCAP’s impact on 15 non-attainment cities in Uttar Pradesh using the Prophet forecasting model. MethodsMonthly data from 2016 to 2023 on AQI and PM10 concentrations were sourced from the Uttar Pradesh Pollution Control Board. Significant changes in mean AQI and PM10 levels from 2017 to 2023 were evaluated using the Friedman Test. Prophet models forecasted PM10 concentrations for 2025-26, with relative percentage changes calculated and model evaluation metrics assessed. FindingsMost cities exhibited unhealthy air quality. Jhansi had the lowest AQI (72·73) in 2023, classified as 'moderate' by WHO standards. Gorakhpur consistently showed 'poor' AQI levels, peaking at 249·31 in 2019. Western Uttar Pradesh cities such as Ghaziabad, Noida, and Moradabad had significant pollution burdens. Predictions showed Bareilly with over a 70% reduction in PM10 levels, Raebareli 58%, Moradabad 55%, Ghaziabad 48%, Agra around 41%, and Varanasi 40%, meeting NCAP targets. However, Gorakhpur and Prayagraj predicted increases in PM10 levels by 50% and 32%, respectively. Moradabad’s model showed the best performance with an R2 of 0·81, MAE of 17·27 μg/m3, and MAPE of 0·10. InterpretationForecasting PM10 concentrations in Uttar Pradesh's non-attainment cities offers policymakers substantial evidence to enhance current efforts. While existing measures are in place, our findings suggest that intensified provisions may be necessary for cities predicted to fall short of meeting program targets. The Prophet model's forecasts can pinpoint these at-risk areas, allowing for targeted interventions and regional adjustments to strategies. This approach will help promote sustainable development customized to each city's specific needs. FundingNo funding was issued for this research.

How Do Forecast Model Biases Affect Large-Scale Teleconnections That Control Southwest U.S. Precipitation? Part I: S2S Models

Abstract We analyze biases in subseasonal forecast models and their effect on Southwest United States (SWUS) precipitation prediction (2–6-week time scale). Cluster analyses identify three primary wave trains associated with SWUS precipitation: a meridional El Niño–Southern Oscillation (ENSO)–type wave train, an arching Pacific–North American (PNA)–type wave train, and a circumglobal zonal wave train. Compared to reanalysis, the models overrepresent the arching pattern, underrepresent the zonal pattern, and produce mixed results for the meridional pattern. The arching pattern overrepresentation is linked to model mean flow biases in the midlatitude–subpolar North Pacific, which cause a westward retraction of the region of forbidden linear Rossby wave propagation. The zonal pattern underrepresentation is linked to westerly biases in the subtropical jet, which cause a westward retraction of the waveguide in the midlatitude eastern North Pacific and divert wave trains southward. These results are confirmed using linear, barotropic ray-tracing analysis. In addition to mean state biases, the models also contain errors in their representation of the Madden–Julian oscillation (MJO). Tropical convection anomalies associated with the MJO are too weak and incoherent at lead times greater than 2 weeks when compared to reanalysis. Additionally, there is a strong SWUS precipitation signal as far out as 5 weeks after a strong MJO in reanalysis, associated with its persistent eastward propagation, but this signal is absent in the models. Our results indicate that there is still significant room for improvement in subseasonal predictions if we can reduce model biases in the background flow and improve the representation of the MJO.

A CNN-LSTM Model for Short-Term Passenger Flow Forecast Considering the Built Environment in Urban Rail Transit Stations

A CNN-LSTM Model for Short-Term Passenger Flow Forecast Considering the Built Environment in Urban Rail Transit Stations

Ensemble Predictability of Week 3/4 Precipitation and Temperature over the United States via Cluster Analysis of the Large-Scale Circulation

Abstract Forecasting the week 3/4 period presents many challenges, resulting in a need for improvements to forecast skill. At this distance from initial conditions, numerical models struggle to present skillful forecasts of temperature, precipitation, and associated extremes. One approach to address this is to utilize more predictable large-scale circulation regimes to make forecasts of temperature and precipitation anomalies, using the association between the regimes and surface weather obtained from reanalysis products. This study explores the utility of k-means cluster analysis on geopotential heights and their ability to make skillful regime predictions in the week 3/4 period. Using 14-day running means of European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA5) 500-hPa geopotential heights for the wintertime December–February (DJF) period, circulation regimes are identified using k-means clustering. Each period is assigned a cluster number, allowing the compositing of any reanalysis or observation variable to form cluster maps. Maps of 500-hPa height, 2-m temperature, precipitation, and storm-track anomalies are some of the variables composited. The utility of these relationships in a dynamical forecast setting is tested via Global Ensemble Forecast System v12 (GEFSv12) hindcasts and real-time ensemble suite forecasts. Week 3/4 deterministic and probabilistic experimental forecasts are then derived from cluster assignments using several methods. We find, via a conditional skill analysis, forecasts strongly correlated with a cluster exhibit greater skill for both dynamical model and cluster-derived forecasts. Our preliminary results represent a step forward to aid forecasters make more skillful assessments of the circulation regime and its associated surface weather for this challenging forecast time scale. Significance Statement Our paper links the local statistics of 14-day precipitation and temperature within the United States to very broad preferred patterns of winds and pressure over the wide Pacific–North American region. Such patterns, known as “circulation regimes,” provide the background that heavily influences local surface conditions. We find that forecasts for which midatmospheric anomalies are closely aligned with one or more circulation regimes are better at predicting the U.S. weather probabilities than other week 3–4 forecasts. A tool based on this finding identifies forecasts for which confidence is higher. Future work will be designed to further exploit the links between circulation regimes and weather to improve the accuracy of week 3–4 forecasts.

Hybrid real-time wave forecasting model combining Gaussian process regression and neural networks

To safely and effectively conduct offshore operations, a real-time wave forecast model that can accurately and rapidly forecast waves hours or days in advance is highly valuable. Recent studies have investigated models with neural networks (NNs) and have demonstrated their advantages in terms of forecasting speed and accuracy. However, NNs are limited by their requirement for a large training dataset, which can hinder the construction of the model if sufficient training data are unavailable. Therefore, this study constructed a hybrid real-time wave forecast model by combining an NN model and Gaussian process regression (GPR) model. The objective was to obtain reasonably accurate forecasts that were more accurate than those from individual NN models even when sufficient training data for the NN model was unavailable. To derive an optimal prediction, the values predicted by the NN and GPR models were combined by utilizing the standard deviation of the latter and considering the accuracy of each model. Consequently, predictions that were more accurate compared to those of the individual models for an entire year were obtained. This improvement was particularly significant for predicting relatively large wave heights, as indicated by the daily maximum wave heights. Additionally, various accuracy evaluation metrics demonstrated that despite using a small training dataset, the optimal prediction of the hybrid model was still superior to those of the individual models for predicting the daily maximum wave heights.

Lead-time-dependent calibration of a flood forecasting model

The hydrological models used for flood forecasting purposes are usually first calibrated in simulation mode and then applied for flood forecasting in combination with some data assimilation procedure allowing to update and correct the model in real-time. Such a two-step procedure may not be the best choice to train model parameters for forecasting purposes. An alternative approach is to calibrate the hydrological model separately for each target lead time in the presence of the updating procedure. However, it is unclear whether this approach can provide the most efficient and the most robust forecasts. We compared in this paper three approaches to calibrate the parameters of a flood forecasting model: calibration in simulation mode (i.e. without data assimilation), lead-time-dependent calibration (i.e. with data assimilation), and a procedure combining both approaches. An hourly flood forecasting model (a parsimonious hydrological model combined with a state updating assimilation procedure) was used to produce forecasts for three lead times on 687 catchments and 40,411 flood events in metropolitan France. The performance of the model calibrated with the three approaches was evaluated according to catchment response times and flood rise times. An analysis of parameter robustness was also carried out. The results showed that lead-time-dependent calibration improves performance for catchments characterised by slow temporal dynamics. However, for catchments with fast temporal dynamics, it leads to degraded performance at short lead times and reduces parameter robustness. We found that the combined calibration approach is the best compromise between parameter robustness and performance at all lead times and for all catchment and flood types.

Optimizing multi-time series forecasting for enhanced cloud resource utilization based on machine learning

Due to its flexibility, cloud computing has become essential in modern operational schemes. However, the effective management of cloud resources to ensure cost-effectiveness and maintain high performance presents significant challenges. The pay-as-you-go pricing model, while convenient, can lead to escalated expenses and hinder long-term planning. Consequently, FinOps advocates proactive management strategies, with resource usage prediction emerging as a crucial optimization category. In this research, we introduce the multi-time series forecasting system (MSFS), a novel approach for data-driven resource optimization alongside the hybrid ensemble anomaly detection algorithm (HEADA). Our method prioritizes the concept-centric approach, focusing on factors such as prediction uncertainty, interpretability and domain-specific measures. Furthermore, we introduce the similarity-based time-series grouping (STG) method as a core component of MSFS for optimizing multi-time series forecasting, ensuring its scalability with the rapid growth of the cloud environment. The experiments performed demonstrate that our group-specific forecasting model (GSFM) approach enabled MSFS to achieve a significant cost reduction of up to 44%.

AutoML and Automated Data Science by Democratizing AI through End-to-End Automation

The rise of data-driven decision-making has led to a significant demand for data science and machine learning (ML) solutions across industries. However, developing these solutions requires extensive expertise in data preprocessing, feature engineering, model selection, hyperparameter tuning and evaluation. AutoML (Automated Machine Learning) and Automated Data Science (AutoDS) have emerged as transformative approaches that aim to democratize data science by automating the endto-end ML pipeline. This paper explores the foundational concepts of AutoML, highlighting key techniques and algorithms, such as neural architecture search (NAS), hyperparameter optimization and meta-learning. We delve into AutoDS's broader scope, which seeks to fully automate tasks from data acquisition to deployment. Real-world applications, such as predictive modeling, anomaly detection and time series forecasting, are examined to demonstrate the impact of these technologies. Additionally, the paper analyzes the current frameworks and platforms facilitating automation, including Auto-sklearn, Google AutoML and H2O.ai and evaluates their performance across different tasks. While the potential to accelerate data science workflows and make AI accessible to non-experts is evident, challenges remain, particularly regarding transparency, interpretability and ethical considerations in fully automated systems. This research provides insights into current trends, future opportunities and the transformative role of AutoML and AutoDS in driving innovation in the data science landscape.

Comparing statistical forecasting methods for modelling respiratory virus healthcare demand

Abstract Background Emergency departments in the United Kingdom are under sustained pressure with rising demand for healthcare, gradually decreasing the functionality of the NHS. Forecasts can aid management in the allocation of appropriate healthcare supplies and staffing needs in advance of an expected surge, as well as allow stakeholders to assess most likely outcomes and potential worst-case scenarios. This is especially valuable during the seasonal waves of respiratory infections. Respiratory viruses are usually concentrated in a 6-8 week seasonal surge during the winter period, creating large pressure on the health system. Current forecast models’ performance depreciates significantly after 1 week. This analysis will forecast 6 weeks of respiratory health admissions, comparing traditional and newly emerging forecasting methods. Methods Using 20 years of English hospital admissions data, we evaluated both within season training models (2-20 weeks) and longer term seasonal models (1-10 years), to forecast 6 weeks of hospital admissions. Respiratory admissions were defined according to ARI/ILI ICD10 codes assigned in hospital, stratified by age and region. Models were iteratively constructed, trained and probabilistic forecasts were evaluated across the respiratory season of September to April using Weighted Interval Score. Averages of each model across iterations were generated for comparison. Results For non-seasonal models, shorter model training lengths created better forecast models for 6 weeks. For seasonal models, optimal training length varied across model complexity. Simple models performed best at forecasting 1 week, however performance deteriorated the most as the forecast window increased. Overall, the Prophet model performed best on average at forecasting 6 weeks across all scenarios. Conclusions Applying our framework for optimal model selection in forecasting analysis will help to inform better practice for future health demand modelling. Key messages • This analysis will inform better practice for future health demand forecasting 6 weeks in advance, allowing for preparation of resources for seasonal surges in healthcare demand. • This analysis suggests using models with modest levels of increased complexity over simpler models could significantly improve forecast performance in the short-medium term.

A Novel Approach for Predicting the Mid–Long-Term Radiation Dose in the Case of a Hypothetical STSBO Nuclear Accident for an Operating Nuclear Power Plant

Four severe nuclear accident scenarios have been identified for operating nuclear power plants (ONPPs). However, there is a research gap in predicting the mid–long-term radiation doses for these scenarios. This study aims to address this gap by proposing a novel approach for predicting the mid–long-term radiation dose in the case of a hypothetical short-term station blackout (STSBO) scenario, one of the aforementioned scenarios. Firstly, the Weather Research and Forecasting (WRF) model was coupled with the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) (WRF-HYSPLIT) model to establish an atmospheric transport and diffusion model for airborne radionuclides, and the regularity of the atmospheric transport and diffusion for the airborne radionuclides was determined. Subsequently, the Residual Radioactive Material Guidelines (RESRAD) OFFSITE (RESRAD-OFFSITE) code was utilized to establish a radiation dose model for predicting the mid–long-term radiation dose resulting from the airborne radionuclides, and the evolution of the mid–long-term radiation dose was analyzed. Finally, the proposed approach was applied to an ONPP, and the results were used to predict the mid–long-term public radiation dose. The results indicated that the total radiation dose would be lower than the dose limit recommended by the International Commission on Radiological Protection (1 mSv/yr) from the second month to the 100th year after the hypothetical STSBO nuclear accident, and the total radiation dose would decrease slowly over time. Recommendations are made for offsite emergency response measures. These research findings can assist ONPPs in analyzing their environmental impacts in the event of an STSBO scenario.

Climate projection of tropical cyclone lifetime in the western North Pacific basin

Abstract In this study, the potential changes of tropical cyclone (TC) lifetime in the western North Pacific basin are examined for different future climates. Using homogeneous 9 km-resolution dynamical downscaling with the Weather Research and Forecasting (WRF) model, we show that TC averaged lifetime displays insignificant change under both low and high greenhouse-gas concentration scenarios. However, more noticeable changes in the tails of TC lifetime statistics are captured in our downscaling simulations, with more frequent long-lived TCs (lifetime of 8-11 days) and less short-lived TCs (lifetime of 3-5 days). Unlike present-day simulations, it is found that the correlation between TC lifetime and the Niño index is relatively weak and insignificant in all future downscaling simulations, thus offering little explanation for these changes in TC lifetime statistics based on the El Niño-Southern Oscillation. More detailed analyses of TC track distribution in the western North Pacific basin reveal, nevertheless, a noticeable shift of TC track patterns towards the end of the 21st century. Such a change in TC track climatology results in an overall longer duration of TCs over the open ocean, which is consistent across future scenarios and periods examined in this study. This shift in the TC track pattern is ultimately linked to changes in the Western North Pacific Subtropical High, which retreats to the south during July and to the east during August-September. The results obtained in this study provide new insights into how large-scale circulations can affect TC lifetime in the western North Pacific basin in warmer climates.

Forecasting the future demand on health workforce – the Austrian model

Abstract Background An aging population and upcoming retirements of health care professionals pose new challenges to the Austrian health care system. On the one hand the health care and nursing needs will increase while at the same time a shortage on health care personnel threatens adequate supply. We as the Austrian Public Health Institute estimate the risk of an undersupply with model-based health workforce forecasting and are therefore able to advise the federal states and the ministry of health on early measures. Methods We developed a forecasting model to estimate the future supply and demand. The main influencing factors are the demographic development as well as the upcoming retirements and graduates. Different environments and needs in different federal states (e.g. changing future skill mix) are considered and can be implemented as additional influencing factors. Usually a baseline scenario as a forward projection of the status quo is developed for defined professions and workplace settings. Then depending on the stakeholder needs additional scenarios are calculated and discussed. A usual forecast horizon is 10 to 15 years. Results Model output are time series of yearly workforce personnel numbers (heads and full-time equivalents) and demand in the defined settings. An analysis of possible gaps shows the need for action. Conclusions The Austrian health workforce model is a flexible tool to estimate future needs on health workforce based on different assumptions. But as assumptions and influencing factors must be represented as data, the data collection can be quite laborious. Comprehensive data is the basis for a comprehensive health workforce planning. Further work is needed to develop practical and evidence-based nursing-sensitive indicators for nursing workfoce reportings on a country level. In Austria the health professionals register forms the basis for the description of the nursing workforce structure, but that there is a need for data sources on care effectiveness. Key messages • Comprehensive data is the basis for a comprehensive health workforce planning. • An aging population and upcoming retirements of health care professionals pose new challenges to the Austrian health care system.

Multi-Step-Ahead Time Series Forecasting using Deep Learning and Fuzzy Time Series-based Error Correction Method

Recently time series forecasting has become one of the prime application areas of climatology, economics and industries. Many research works are conducted to forecast the time series more accurately. But few of them are concentrated on predicting the time series over an extended future horizon, and there is also a scope to improve their forecasting accuracy. This work proposes a multi-step-ahead foresting method to produce a stable and accurate forecasting result for the extended future horizon. Firstly, a deep learning-based forecasting model is proposed to predict the time series. Secondly, a fuzzy time series-based error correction model is implemented to enhance the prediction performance of the deep learning model. Here to optimize all the fuzzy time series (FTS) parameters in an integrated way, an integrated butterfly optimization (FTS-IBO) algorithm is proposed. In this study, two different types of real-world multivariate time series datasets are used to analyze the forecasting performance of the proposed model. The performance of the proposed FTS-IBO algorithm is compared with the traditional butterfly optimization (FTS-BO) algorithm. The experimental results show that the FTS-IBO technique is superior to the FTS-BO technique. The forecasting performance of the proposed model has also compared the other state-of-the-art models, and the simulation results exhibit that the proposed model produces a more accurate prediction performance for multi-step-ahead time series forecasting problems compared to other models.

Maintaining Agricultural Production Profitability—A Simulation Approach to Wheat Market Dynamics

Profitability is a complex notion, intertwined with theoretical, socio-economic, and modern economic considerations. While traditionally linked to monopoly advantages, contemporary views broaden this to include shareholder value creation. In agriculture, profitability faces impediments such as rising input costs, market volatility, and economic disparities, emphasizing technological innovation, market conditions, and policy as crucial determinants. This study presents an econometric simulation model to analyze and forecast wheat production profitability in Poland from 2023 to 2027, providing insights into production efficiencies, market dynamics, and policy impacts. Using a system of recursive equations, the model forecasts profitability, integrating econometric techniques with expert insights. It examines variables like resource use, production levels, and price changes, validated through FADN data. The findings reveal that price relations and production efficiencies are the key to profitability. The model highlights market volatility, particularly through “price scissors”, as the main factor influencing profitability. Favorable price conditions significantly boost profitability and incentivize production. The model is a critical tool for analyzing wheat profitability, highlighting the influence of market and policy changes. Future work could extend the model’s use to other regions or crops, incorporating advanced technologies to improve accuracy. This study offers valuable insights for agricultural economics, aiding stakeholders in strategic decision-making.

An Improved Neural Network Algorithm for Energy Consumption Forecasting

Accurate and efficient forecasting of energy consumption is a crucial prerequisite for effective energy planning and policymaking. The BP neural network has been widely used in forecasting, machine learning, and various other fields due to its nonlinear fitting ability. In order to improve the prediction accuracy of the BP neural network, this paper introduces the concept of forecast lead time and establishes a mathematical model accordingly. Prior to training the neural network, the input layer data are preprocessed based on the forecast lead time model. The training and forecasting results of the BP neural network when and when not considering forecast lead time are compared and verified. The findings demonstrate that the forecast lead time model can significantly improve the prediction speed and accuracy, proving to be highly applicable for short-term energy consumption forecasting.

Time Series Forecasting of Thermal Systems Dispatch in Legal Amazon Using Machine Learning

This paper analyzes time series forecasting methods applied to thermal systems in Brazil, specifically focusing on diesel consumption as a key determinant. Recognizing the critical role of thermal systems in ensuring energy stability, especially during low rain seasons, this study employs bagged, boosted, and stacked ensemble learning methods for time series forecasting focusing on exploring consumption patterns and trends. By leveraging historical data, the research aims to predict future diesel consumption within Brazil’s thermal energy sector. Based on the bagged ensemble learning approach a mean absolute percentage error of 0.089% and a coefficient of determination of 0.9752 were achieved (average considering 50 experiments), showing it to be a promising model for the short-time forecasting of thermal dispatch for the electric power generation system. The bagged model results were better than for boosted and stacked ensemble learning methods, long short-term memory networks, and adaptive neuro-fuzzy inference systems. Since the thermal dispatch in Brazil is closely related to energy prices, the predictions presented here are an interesting way of planning and decision-making for energy power systems.

Evaluation of WRF model performance with different microphysics schemes for extreme rainfall prediction in Lagos, Nigeria: Implications for urban flood risk management

In Nigeria, particularly in urban areas like Lagos, flooding is a frequent natural hazard. In 2011, Lagos experienced one of its worst floods resulting in significant economic losses and displacement of people. In recent years, Lagos has continued to grapple with flooding challenges, with an equally significant flood episode occurring in 2021. This study focuses on predicting floods and forecasting extremely heavy rainfall in West Africa's equatorial zone using the Weather Research and Forecasting (WRF) model, particularly in humid tropical environments like Lagos. The study discusses the need to review existing flood models and adopt alternative flood models to address the limitations of flood prediction. As potential causes of these rainfall episodes, the interconnections between synoptic systems such as tropical easterly waves, southwesterly winds related to the West African Monsoon, and local topography and oceanic conditions are investigated. Three key metrics: root mean square error (RMSE), mean bias (MB), and mean absolute error (MAE) are used to assess the effectiveness of the computational model. Results indicate that the WRF model, specifically when using the Thompson parameterisation, can estimate the amount of rainfall accumulated over a 24-h period. This suggests that the model can predict the size of daily precipitation during intense rain events. The Thompson scheme shows better performance compared to the WSM6 scheme while evaluating the stations and episodes. During the rainfall episode on July 10, 2011, Thompson's spatial rainfall predictions were better than WSM6, resulting in a decrease in root mean square error (RMSE) of 15–31% depending on the area. Simulations of the July 2021 episode also show better performance, with a decrease in RMSE of 11–25% when comparing Thompson to WSM6 scheme. The Thompson scheme’s improved ability is directly linked to a more accurate depiction of the microphysical mechanisms that control the rainfall formation. By explicitly simulating the dynamics of ice crystals and graupel, it is possible to accurately replicate the processes of orographic lifting and moist convection that are responsible for driving intense monsoon precipitation. In addition, Thompson scheme shows a reduced degree of systemic bias in comparison to WSM6, with a 75% reduction in the average bias in rainfall accumulation over the research area. The combination of the advanced Thompson microphysics method and WRF's atmospheric dynamics shows a high level of accuracy in predicting intense rainfall and the risk of floods in this area with diverse tropical topography.

Significant of Blood Glucose Time-Series Forecasting with Temporal Fusion Transformer Model for Type 1 Diabetes

Blood glucose (BG) prediction has advanced to a state of the art thanks to deep learning models, which have been demonstrated to enhance type 1 diabetes (T1D) therapy. Nevertheless, most current models are limited to single-horizon prediction and have several practical drawbacks, including poor interpretability. In this study, we develop a novel approach to optimize the forecasting model in the training processes using the Optuna function, cross-validation, and the latest dataset. We offer a novel deep learning framework for multi-horizon BG prediction: the temporal fusion transformer (TFT). TFT employs a self-attention mechanism to extract long-term temporal dependencies and enables a model with an auto-tuning adjustment approach on hyperparameters and a cross-validation function on univariate and multivariate input models. On a clinical dataset with T1D subjects, namely ShanghaiT1DM (16 subjects), D1NAMO (9 subjects), and OhioT1DM (6 subjects) datasets, it achieved an average root mean square error (RMSE) of the three datasets used. The TFT model gave a lower error score than the baseline models of neural hierarchical interpolation for time series (N-HiTS)and long short-term memory (LSTM) with the values of 10.08+0.31 mg/dL and 12.34+0.62 mg/dL on the univariate input model and 9.18+1.21 mg/dL and 14.33+0.52 mg/dL on the multivariate input model for the prediction horizons of 30 and 60 minutes, respectively. This result explained that the TFT model was adequate for carrying out multi-horizon BG value-level forecasting and potentially deploying on-edge devices to improve clinical efforts to manage BG levels for T1D patients in real-time application.

Modeling of hybrid renewable resources and comprehensive feasibility analysis of grid interactive energy system for commercial building: a case study

ABSTRACT The growing energy demand in commercial buildings and businesses poses significant environmental challenges due to the dependence on fossil fuel-based energy. To address this issue, increasing the use of renewable energy sources and decreasing reliance on fossil fuels is crucial. Commercial buildings, in particular, have many opportunities to harness energy from renewable resources, such as solar, wind, and biomass. This study analyzed historical energy consumption data from a commercial building using MATLAB to forecast future energy demands. A range of machine learning algorithms were employed to accurately predict energy consumption, including Ensemble Boosting, Ensemble Bagging, Decision Trees, Support Vector Machines (SVM), and Neural Networks (NN). A Hybrid Renewable Energy (HRE) system was designed to meet the building’s energy needs, integrating renewable sources with grid-interactive inverters. Performance metrics for the forecasting models were calculated and documented, followed by a detailed techno-economic and environmental feasibility analysis of the HRE system. Different levels of renewable energy penetration were explored to optimize HRE designs using load-following dispatch algorithms tailored to the commercial building’s specific needs. The study revealed that achieving a renewable energy proportion of 12.6% resulted in the lowest cost of electricity (COE) at $0.135 and a Net Present Cost (NPC) of $8.84 million. Additionally, a reliability study using load flow techniques confirmed the stability of the optimized Hybrid Renewable Energy system. The energy costs for varying levels of renewable penetration, ranging from 10% to 40% is also explored in this research. In summary, transitioning to renewable energy sources in commercial buildings can reduce environmental impact while ensuring a sustainable energy supply.

Climatology of Orographic Precipitation Gradients Over High Mountain Asia Derived From Dynamical Downscaling

AbstractWithin High Mountain Asia (HMA), the annual melting of glaciers and snowpack provides vital freshwater to populations living downstream. Precipitation over HMA can directly affect the freshwater availability in this region by altering the mass balance of glaciers and snowpack. However, available reanalyses and downscaling simulations lack the resolution required to understand important glacier‐scale variations in precipitation. This study aimed to determine the current characteristics of orographic precipitation gradients (OPG) by curve‐fitting daily precipitation as a function of elevation from a 15‐year, 4‐km grid spaced Weather Research and Forecasting (WRF) model simulation focused on the Himalayan, Karakoram, and Hindu‐Kush mountain ranges. To facilitate precipitation curve‐fitting, the WRF model grid points were separated into regions of similar orientation, referred to as facets. Akaike Information Criterion‐corrected values and an F‐test p‐value identified the need for a curvature term to account for a varying OPG with elevation. Regions with similar seasonal variability were found using ‐means clustering of the monthly mean OPG coefficients. The central Himalayan slope's intra‐seasonal variability of OPG depended on synoptic scale conditions, in which cyclonically‐forced heavy‐precipitation events produced strong sublinear increases in precipitation with elevation. Initial testing of precipitation estimates using monthly coefficients showed promising results in downscaling daily WRF precipitation; the daily mean absolute error at each grid point had a lower magnitude than the daily mean precipitation total, on average. Results provide a physically‐based context for machine learning algorithms being developed to predict OPG and downscale precipitation output from global climate models over HMA.