Multi-objective LSTM ensemble model for household short-term load forecasting

IF 3.3 2区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Memetic Computing Pub Date : 2022-01-25 DOI:10.1007/s12293-022-00355-y

Fan, Chaodong, Li, Yunfan, Yi, Lingzhi, Xiao, Leyi, Qu, Xilong, Ai, Zhaoyang

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引用次数: 1

Abstract

With the development of smart grid, household load forecasting played an important role in power system operations. However, the household load forecasting is often inefficient due to its high volatility and uncertainty. Consequently, a multi-objective LSTM ensemble model based on the DBN combination strategy, is proposed in this paper. This method first builds a deep learning framework based on the LSTM network in order to generate several sub-models. With the diversity and accuracy of the sub-models as the objective functions, the improved MOEA/D algorithm is then used to optimize the parameters, in order to enhance the overall performance of the sub-models and ensure their differences. Finally, a DBN-based combination strategy is used to combine the single forecasts in order to form the ensemble forecast, and reduce the adverse effects of model uncertainty and data noise on the prediction accuracy. The experimental results show that the proposed method has several advantages in prediction accuracy and generalization capacity, compared with several current intelligent prediction methods.

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家庭短期负荷预测的多目标LSTM集成模型

随着智能电网的发展，家庭负荷预测在电力系统运行中发挥着重要作用。然而，由于家庭负荷预测具有较高的波动性和不确定性，因此往往效率低下。为此，本文提出了一种基于DBN组合策略的多目标LSTM集成模型。该方法首先构建基于LSTM网络的深度学习框架，生成多个子模型。以子模型的多样性和准确性为目标函数，采用改进的MOEA/D算法对参数进行优化，以提高子模型的整体性能，保证子模型的差异性。最后，采用基于dbn的组合策略，将单个预测组合起来形成集合预测，降低模型不确定性和数据噪声对预测精度的不利影响。实验结果表明，与现有的几种智能预测方法相比，该方法在预测精度和泛化能力方面具有一定的优势。

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来源期刊

Memetic Computing COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-OPERATIONS RESEARCH & MANAGEMENT SCIENCE

CiteScore

6.80

自引率

12.80%

发文量

期刊介绍： Memes have been defined as basic units of transferrable information that reside in the brain and are propagated across populations through the process of imitation. From an algorithmic point of view, memes have come to be regarded as building-blocks of prior knowledge, expressed in arbitrary computational representations (e.g., local search heuristics, fuzzy rules, neural models, etc.), that have been acquired through experience by a human or machine, and can be imitated (i.e., reused) across problems. The Memetic Computing journal welcomes papers incorporating the aforementioned socio-cultural notion of memes into artificial systems, with particular emphasis on enhancing the efficacy of computational and artificial intelligence techniques for search, optimization, and machine learning through explicit prior knowledge incorporation. The goal of the journal is to thus be an outlet for high quality theoretical and applied research on hybrid, knowledge-driven computational approaches that may be characterized under any of the following categories of memetics: Type 1: General-purpose algorithms integrated with human-crafted heuristics that capture some form of prior domain knowledge; e.g., traditional memetic algorithms hybridizing evolutionary global search with a problem-specific local search. Type 2: Algorithms with the ability to automatically select, adapt, and reuse the most appropriate heuristics from a diverse pool of available choices; e.g., learning a mapping between global search operators and multiple local search schemes, given an optimization problem at hand. Type 3: Algorithms that autonomously learn with experience, adaptively reusing data and/or machine learning models drawn from related problems as prior knowledge in new target tasks of interest; examples include, but are not limited to, transfer learning and optimization, multi-task learning and optimization, or any other multi-X evolutionary learning and optimization methodologies.