在智能电网中优化太阳能效率,使用混合机器学习模型进行精确的能源生产预测
Muhammad Shoaib Bhutta1, Yang Li2, Muhammad Abubakar3
1School of Automobile Engineering, Guilin University of Aerospace Technology, Guilin, 541004, China. shoaibbhutta@hotmail.com.
Scientific reports
|July 24, 2024
概括
混合机器学习模型准确地预测了智能电网的太阳能发电量. 这提高了可再生能源的整合和电网效率,这对于第四次能源革命至关重要.
科学领域:
- 能源系统工程 能源系统工程
- 人工智能的人工智能
- 可再生能源技术可再生能源技术
背景情况:
- 第四次能源革命将可再生能源整合到智能电网中.
- 可变的可再生能源产量取决于天气,这给整合带来了挑战.
- 智能电网利用人工智能和实时数据来优化能源生产和分配.
研究的目的:
- 在智能电网中提高太阳能发电效率.
- 评估混合机器学习模型来预测太阳能发电厂的性能.
主要方法:
- 开发并测试了混合机器学习模型:混合卷积-反复网络 (HCRN),混合卷积-LSTM网络 (HCLN) 和混合卷积-GRU网络 (HCGRN).
- 使用的太阳能电站数据包括发电量 (MWh),阵列辐射平面 (POA) 和性能比率 (PR).
主要成果:
- 混合卷积式LSTM网 (HCLN) 模型实现了卓越的准确性.
- 在MWh,POA和PR预测中,HCLN展示了最低的根平均平方误差 (RMSE) 和平均绝对误差 (MAE).
- 具体的RMSE值为:0.012027 (MWh),0.013734 (POA),0.003055 (PR) 的值. 这些值为:0.012027 (MWh),0.013734 (POA),0.003055 (PR). 具体的MAE值为:0.069523 (MWh),0.082813 (POA),0.042815 (PR). 它们可以分为两种.
结论:
- 拟议的混合机器学习模型有效地提高了太阳能发电系统的效率.
- 精确预测关键的太阳能电站测量是可以实现的这些模型.
- 这项研究支持将可再生能源纳入智能电网.
相关概念视频
Wind Turbine Machine Models
120
In the growing field of wind energy, incorporating wind turbine models into transient stability analysis is essential. Induction and synchronous machines are the primary models used, with induction machines being prevalent due to their simplicity and reliability.
Induction machines interact through the rotating magnetic field generated by the stator and the rotor. The key parameter is slip, which is the difference between synchronous speed and rotor speed relative to synchronous speed. Slip is...
Induction machines interact through the rotating magnetic field generated by the stator and the rotor. The key parameter is slip, which is the difference between synchronous speed and rotor speed relative to synchronous speed. Slip is...
120
Simplified Synchronous Machine Model
205
The Synchronous Machine Model is a fundamental tool in analyzing and ensuring the transient stability of power systems. This model simplifies the representation of a synchronous machine under balanced three-phase positive-sequence conditions, assuming constant excitation and ignoring losses and saturation. The model is pivotal for understanding the behavior of synchronous generators connected to a power grid, particularly during transient events.
In this model, each generator is connected to a...
In this model, each generator is connected to a...
205
Maximum Power Flow and Line Loadability
106
The maximum power flow for lossy transmission lines is derived using ABCD parameters in phasor form. These parameters create a matrix relationship between the sending-end and receiving-end voltages and currents, allowing the determination of the receiving-end current. This relationship facilitates calculating the complex power delivered to the receiving end, from which real and reactive power components are derived.
106
Multimachine Stability
150
Multimachine stability analysis is crucial for understanding the dynamics and stability of power systems with multiple synchronous machines. The objective is to solve the swing equations for a network of M machines connected to an N-bus power system.
In analyzing the system, the nodal equations represent the relationship between bus voltages, machine voltages, and machine currents. The nodal equation is given by:
In analyzing the system, the nodal equations represent the relationship between bus voltages, machine voltages, and machine currents. The nodal equation is given by:
150
Fast Decoupled and DC Powerflow
182
The fast decoupled power flow method addresses contingencies in power system operations, such as generator outages or transmission line failures. This method provides quick power flow solutions, essential for real-time system adjustments. Fast decoupled power flow algorithms simplify the Jacobian matrix by neglecting certain elements, leading to two sets of decoupled equations:
182
Control of Power Flow
262
There are several methods to control power flow in power systems:
262


