通过利用长期短期记忆循环神经网络,加强基于数据的负载预测
Waqar Waheed1, Qingshan Xu1, Muhammad Aurangzeb1
1Department of Electrical Engineering, Southeast University, Nanjing, Jiangsu, 210096, People's Republic of China.
Heliyon
|January 6, 2025
概括
本研究引入了长期短期记忆循环神经网络 (LSTM-RNN) 模型,用于准确预测功率负载. 该模型有效预测能源需求,增强智能电网稳定性和运营效率.
科学领域:
- 电气工程 电气工程
- 计算机科学 计算机科学
- 数据科学数据科学数据科学
背景情况:
- 可再生能源的日益集成使得电力系统管理变得更加复杂.
- 准确的负载预测对于电网稳定至关重要,考虑到动态的气候和社会经济因素.
- 传统的方法难以应对负载数据的复杂时间动态.
研究的目的:
- 开发和评估一个长期短期记忆循环神经网络 (LSTM-RNN) 模型,用于精确的功率负载预测.
- 评估模型在捕捉负载数据中复杂的时间关系方面的表现.
- 为了证明该模型在将需求响应与可再生能源整合到智能电网中的实用性.
主要方法:
- 实现长期短期记忆循环神经网络 (LSTM-RNN) 架构.
- 使用历史功率负载数据对LSTM-RNN模型进行培训和验证.
- 使用平均绝对百分比误差 (MAPE) 和根平均平方误差 (RMSE) 评估预测准确度.
主要成果:
- 在每小时负载预测中,LSTM-RNN模型实现了1.5%的MAPE和26.5的RMSE.
- 年度负载估计显示1.77%的MAPE和30个RMSE.
- 每小时的预测模型在对抗杂或不充分的输入数据方面表现出卓越的准确性和稳定性.
结论:
- LSTM-RNN为准确的功率负载预测提供了实用和高效的解决方案.
- 该模型提高了电力系统的运营效率和弹性.
- 这项技术对于有效的需求响应和智能电网稳定性与分布式可再生能源至关重要.
相关概念视频
01:22Chunking and Rehearsal in Sensory Memory
105
Improving short-term memory can be achieved through techniques like chunking and rehearsal. Chunking involves organizing information into larger, more manageable units. This technique is particularly useful for information that exceeds the typical memory span of between five and nine items. For instance, logging into an online account with a password like "ta89vq0179gz" involves grouping letters and numbers into three chunks—ta89, vq01, and 79gz. It makes large amounts of...
105
01:35Long-term Potentiation
54.4K
Long-term potentiation, or LTP, is one of the ways by which synaptic plasticity—changes in the strength of chemical synapses—can occur in the brain. LTP is the process of synaptic strengthening that occurs over time between pre- and postsynaptic neuronal connections. The synaptic strengthening of LTP works in opposition to the synaptic weakening of long-term depression (LTD) and together are the main mechanisms that underlie learning and memory.
54.4K
01:19Survival Tree
37
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
Building a Survival Tree
Constructing a...
Building a Survival Tree
Constructing a...
37
01:05Long-term Depression
30.3K
Long-term depression, or LTD, is one of the ways by which synaptic plasticity—changes in the strength of chemical synapses—can occur in the brain. LTD is the process of synaptic weakening that occurs over time between pre and postsynaptic neuronal connections. The synaptic weakening of LTD works in opposition to synaptic strengthening by long-term potentiation (LTP) and together are the main mechanisms that underlie learning and memory.
30.3K
01:24Working Memory
82
Working memory refers to a combination of components, including short-term memory and attention, that allow an individual to hold information temporarily as we perform cognitive tasks. It is an essential cognitive function that enables the execution of complex tasks such as problem-solving, comprehension, and reasoning. Unlike short-term memory, which simply involves the storage of information for a brief period, working memory involves the active manipulation and processing of this...
82
01:12Chunking
32
Chunking is a powerful cognitive technique that improves short-term memory retention by organizing information into smaller, more manageable units. The brain, limited by working memory capacity, can more easily process and store information when it is divided into "chunks" rather than presented as discrete, unrelated elements. Chunking is especially useful when dealing with large amounts of information, such as numerical sequences, words, or complex ideas.
The principle behind chunking...
The principle behind chunking...
32