A hybrid photovoltaic/wind power prediction model based on Time2Vec, WDCNN and BiLSTM

Energy Conversion and Management, Journal Year: 2023, Volume and Issue: 291, P. 117342 - 117342

Published: July 1, 2023

Language: Английский

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The Implications of Long-Term Local Climate Change for the Energy Performance of an nZEB Residential Building in Volos, Greece

Energies, Journal Year: 2025, Volume and Issue: 18(5), P. 1032 - 1032

Published: Feb. 20, 2025

The construction of nearly zero-emission buildings in Europe and internationally has become mandatory by legislation. In parallel with these developments, the non-reversible increase ambient temperatures stresses buildings’ energy systems during summer months extreme temperatures, their severity varying according to local microclimate. These phenomena result an cooling loads. Thus, HVAC system’s performance needs more careful study, especially for residential sector wherever night effect is no longer capable releasing stress. present work, impact climate change on a building’s studied through simulations. future increases intensity duration heat waves assessed exploiting long-term forecasting capabilities transformer neural network model, trained existing meteorological data period 2007–2023. Based forecasted climatic conditions 2030 2040 produced this way, projected effects are assessed. forecast was aided 43 years temperature Europe, available ERA5 Copernicus program datasets. respective predictions electricity consumption wave episodes long durations point necessity special measures keep internal grid’s autonomy reduce unwanted interactions external grid. Moreover, further improvements nZEB building design improved would be critical success policy next two decades.

Language: Английский

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BMAE-Net: A Data-Driven Weather Prediction Network for Smart Agriculture

Agronomy, Journal Year: 2023, Volume and Issue: 13(3), P. 625 - 625

Published: Feb. 22, 2023

Weather is an essential component of natural resources that affects agricultural production and plays a decisive role in deciding the type production, planting structure, crop quality, etc. In field agriculture, medium- long-term predictions temperature humidity are vital for guiding activities improving yield quality. However, existing intelligent models still have difficulties dealing with big weather data predicting applications, such as striking balance between prediction accuracy learning efficiency. Therefore, multi-head attention encoder-decoder neural network optimized via Bayesian inference strategy (BMAE-Net) proposed herein to predict time series changes accurately. Firstly, we incorporate into gated recurrent unit construct Bayesian-gated units (Bayesian-GRU) module. Then, mechanism introduced design structure each layer, applicability time-length changes. Subsequently, framework hyperparameter optimization designed infer intrinsic relationships among time-series high accuracy. For example, R-evaluation metrics three locations 0.9, 0.804, 0.892, respectively, while RMSE reduced 2.899, 3.011, 1.476, seen Case 1 data. Extensive experiments subsequently demonstrated BMAE-Net has overperformed on location datasets, which provides effective solution applications smart agriculture system.

Language: Английский

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Analysis, characterization, prediction, and attribution of extreme atmospheric events with machine learning and deep learning techniques: a review

Theoretical and Applied Climatology, Journal Year: 2023, Volume and Issue: 155(1), P. 1 - 44

Published: Aug. 28, 2023

Abstract Atmospheric extreme events cause severe damage to human societies and ecosystems. The frequency intensity of extremes other associated are continuously increasing due climate change global warming. accurate prediction, characterization, attribution atmospheric is, therefore, a key research field in which many groups currently working by applying different methodologies computational tools. Machine learning deep methods have arisen the last years as powerful techniques tackle problems related events. This paper reviews machine approaches applied analysis, most important extremes. A summary used this area, comprehensive critical review literature ML EEs, provided. has been extended rainfall floods, heatwaves temperatures, droughts, weather fog, low-visibility episodes. case study focused on analysis temperature prediction with DL is also presented paper. Conclusions, perspectives, outlooks finally drawn.

Language: Английский

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Modelling monthly rainfall of India through transformer-based deep learning architecture

Modeling Earth Systems and Environment, Journal Year: 2024, Volume and Issue: 10(3), P. 3119 - 3136

Published: Feb. 8, 2024

Language: Английский

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SA–EMD–LSTM: A novel hybrid method for long-term prediction of classroom PM2.5 concentration

Expert Systems with Applications, Journal Year: 2023, Volume and Issue: 230, P. 120670 - 120670

Published: June 3, 2023

Language: Английский

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A Novel WD-SARIMAX Model for Temperature Forecasting Using Daily Delhi Climate Dataset

Sustainability, Journal Year: 2022, Volume and Issue: 15(1), P. 757 - 757

Published: Dec. 31, 2022

Forecasting is defined as the process of estimating change in uncertain situations. One most vital aspects many applications temperature forecasting. Using Daily Delhi Climate Dataset, we utilize time series forecasting techniques to examine predictability temperature. In this paper, a hybrid model based on combination Wavelet Decomposition (WD) and Seasonal Auto-Regressive Integrated Moving Average with Exogenous Variables (SARIMAX) was created accomplish accurate for Delhi, India. The range dataset from 2013 2017. It consists 1462 instances four features, 80% data used training 20% testing. First, WD decomposes non-stationary into multi-dimensional components. That can reduce original series’ volatility increase its stability. After that, components are inputs SARIMAX forecast City. employed work has following order: (4, 0, 1). [1], 12). experimental results demonstrated that WD-SARIMAX performs better than other recent models city. Mean Square Error (MSE), Absolute (MAE), Median (MedAE), Root (RMSE), Percentage (MAPE), determination coefficient (R2) proposed 2.8, 1.13, 0.76, 1.67, 4.9, 0.91, respectively. Furthermore, utilized over next eight years, 2017 2025.

Language: Английский

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A Hybrid Deep Learning Model for Air Quality Prediction Based on the Time–Frequency Domain Relationship

Atmosphere, Journal Year: 2023, Volume and Issue: 14(2), P. 405 - 405

Published: Feb. 20, 2023

Deep learning models have been widely used in time-series numerical prediction of atmospheric environmental quality. The fundamental feature this application is to discover the correlation between influencing factors and target parameters through a deep network structure. These relationships original data are affected by several different frequency factors. If adopted without guidance, these correlations may be masked entangled multifrequency data, which will cause problem insufficient extraction difficult model interpretation. Because wavelet transform has ability separate can extracted methods, hybrid combining transformer-like (WTformer) was designed extract time–frequency domain features air 2018–2021 hourly Guilin as benchmark training dataset. Pollutants meteorological variables local dataset decomposed into five bands wavelet. analysis WTformer showed that particulate matter (PM2.5 PM10) had an obvious low-frequency band low high-frequency band. PM2.5 temperature negative positive wind speed results laws could found model, made it possible explain model. experimental show performance established better than multilayer perceptron (MLP), one-dimensional convolutional neural (1D-CNN), gate recurrent unit (GRU), long short-term memory (LSTM) Transformer, all time steps (1, 4, 8, 24 48 h).

Language: Английский

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A functional autoregressive approach for modeling and forecasting short-term air temperature

Frontiers in Environmental Science, Journal Year: 2024, Volume and Issue: 12

Published: May 22, 2024

A precise forecast of atmospheric temperatures is essential for various applications such as agriculture, energy, public health, and transportation. Modern advancements in technology have led to the development sensors other tools collect high-frequency air temperature data. However, accurate forecasts are challenging due their specific features including high dimensionality, non-linearity, seasonal dependency, etc. To address these forecasting challenges, this study proposes a functional modeling framework based on components estimation technique by partitioning time series into deterministic stochastic components. The component that comprises daily yearly seasonalities modeled forecasted using generalized additive techniques. Similarly, accounts short-term dynamics process autoregressive model, integrated moving average, vector models. evaluate performance models, hourly data collected from Islamabad, Pakistan, one-day-ahead out-of-sample obtained complete year. results all models compared root mean squared error, absolute percentage error. suggest proposed FAR model performs relatively well ARIMA VAR resulting lower errors. findings research can facilitate informed decision-making across sectors, optimize resource allocation, enhance safety, promote socio-economic resilience.

Language: Английский

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Predicting temperature variability in major Indian cities using Random Forest Regression (RFR) Model

Journal of Earth System Science, Journal Year: 2025, Volume and Issue: 134(1)

Published: Jan. 28, 2025

Language: Английский

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