Renewable energy as an alternative source for energy management in agriculture

Energy Reports, Journal Year: 2023, Volume and Issue: 10, P. 344 - 359

Published: July 3, 2023

This study provides a high-level overview of alternative energy sources that can be harnessed to power agricultural operations, focusing on renewable technologies. When thinking about the overall economy around globe, agriculture is vital. Energy required at each step production, from fertilizer production fueling tractors for planting and harvesting. The high prices unpredictable market significantly affect input costs. efficiency methods, when properly applied, use farm's could assist producers in saving energy-related Renewable resources form solar, biomass, wind, geothermal are abundantly available sector. review aims explore as an source efficient management agriculture. It discusses potential benefits, challenges, opportunities associated with adopting technologies Our research adds value by presenting comprehensive their applicability management. By highlighting benefits challenges option, we provide valuable insights stakeholders researchers aiming transition toward sustainable practices Better intertwined problems need broader strategy than has so far been used. In nutshell, transitioning holds great promise reducing greenhouse gas emissions, improving efficiency, promoting sustainability food production. However, successful implementation requires addressing technical, economic, policy barriers while fostering knowledge dissemination capacity building among farmers stakeholders.

Language: Английский

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Optimization of a hybrid solar/wind/storage system with bio-generator for a household by emerging metaheuristic optimization algorithm

Journal of Energy Storage, Journal Year: 2023, Volume and Issue: 73, P. 108967 - 108967

Published: Sept. 17, 2023

Language: Английский

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Optimizing multi-step wind power forecasting: Integrating advanced deep neural networks with stacking-based probabilistic learning

Applied Energy, Journal Year: 2024, Volume and Issue: 369, P. 123487 - 123487

Published: May 30, 2024

Language: Английский

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A novel DWTimesNet-based short-term multi-step wind power forecasting model using feature selection and auto-tuning methods

Energy Conversion and Management, Journal Year: 2024, Volume and Issue: 301, P. 118045 - 118045

Published: Jan. 5, 2024

Language: Английский

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A location-centric transformer framework for multi-location short-term wind speed forecasting

Energy Conversion and Management, Journal Year: 2025, Volume and Issue: 328, P. 119627 - 119627

Published: Feb. 19, 2025

Language: Английский

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Regional wind-photovoltaic combined power generation forecasting based on a novel multi-task learning framework and TPA-LSTM

Energy Conversion and Management, Journal Year: 2023, Volume and Issue: 297, P. 117715 - 117715

Published: Oct. 3, 2023

Language: Английский

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Digital twin of wind farms via physics-informed deep learning

Energy Conversion and Management, Journal Year: 2023, Volume and Issue: 293, P. 117507 - 117507

Published: Aug. 16, 2023

The spatiotemporal flow field in a wind farm determines the turbines' energy production and structural fatigue. However, it is not obtainable by current measurement, modeling, prediction tools industry. Here we propose novel data knowledge fusion approach to create first digital twin for onshore/offshore system, which can predict situ covering entire farm. developed integrating Lidar measurements, Navier–Stokes equations, turbine modeling using actuator disk method, via physics-informed neural networks. design enables seamless integration of measurements operating real-time characterization, physics retrieving unmeasured information. It thus addresses limitations existing approaches based on supervised machine learning, cannot achieve such because training targets are available. Case studies under typical scenarios (i.e. greedy case, wake-steering partially-operating case) carried out high-fidelity numerical experiments, results show that achieves very accurate mirroring physical farm, capturing detailed features as wake interaction meandering. error fields, average, just 4.7% value range. With information predicted, expected enable brand new research across lifecycle including monitoring, control, load assessment.

Language: Английский

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Attention mechanism is useful in spatio-temporal wind speed prediction: Evidence from China

Applied Soft Computing, Journal Year: 2023, Volume and Issue: 148, P. 110864 - 110864

Published: Sept. 26, 2023

Language: Английский

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A Lightweight Framework for Rapid Response to Short-Term Forecasting of Wind Farms Using Dual Scale Modeling and Normalized Feature Learning

Energies, Journal Year: 2025, Volume and Issue: 18(3), P. 580 - 580

Published: Jan. 26, 2025

Accurate wind power forecasting is crucial for optimizing grid scheduling and improving utilization. However, real-world time series exhibit dynamic statistical properties, such as changing mean variance over time, which make it difficult models to apply observed patterns from the past future. Additionally, execution speed high computational resource demands of complex prediction them deploy on edge computing nodes farms. To address these issues, this paper explores potential linear constructs NFLM, a linear, lightweight, short-term model that more adapted characteristics data. The captures both long-term sequence variations through continuous interval sampling. mitigate interference features, we propose normalization feature learning block (NFLBlock) core component NFLM processing sequences. This module normalizes input data uses stacked multilayer perceptron extract cross-temporal cross-dimensional dependencies. Experiments with two real farms in Guangxi, China, showed compared other advanced methods, MSE 24-step ahead respectively reduced by 23.88% 21.03%, floating-point operations (FLOPs) parameter count only require 36.366 M 0.59 M, respectively. results show can achieve good accuracy fewer resources.

Language: Английский

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Quantile-transformed multi-attention residual framework (QT-MARF) for medium-term PV and wind power prediction

Renewable Energy, Journal Year: 2023, Volume and Issue: 220, P. 119604 - 119604

Published: Nov. 8, 2023

Language: Английский

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