Electric load forecasting is a vital role in obtaining effective management of modern power systems. The accuracy forecasting results will lead to the improvement of the energy efficiency and reduction of production c...
详细信息
Electric load forecasting is a vital role in obtaining effective management of modern power systems. The accuracy forecasting results will lead to the improvement of the energy efficiency and reduction of production cost. This paper presents a novel electric load forecasting model by using BP neural network and improved bat algorithm with extremal optimization called IBA-EO-BP model. First, to enhance the global search ability and diversity of original bat algorithm (BA), we propose IBA-EO by improving original BA and combining with extremal optimization. Then, considering traditional BP is more likely converge to local optimal values, the IBA-EO is employed to find out the optimal connection weight parameters in BP. Two datasets from energy market operation in Australia are selected as case study. The simulation results demonstrate that the proposed IBA-EO-BP model is much more accurate than the traditional BP forecasting model and persistence model in terms of three widely used performance indices and two statistical tests.
Firefly algorithm (FA) has widely used to solve various complex optimization problems. However, FA has significant drawbacks in slow convergence rate and easily trapped into local optimum. To tackle these defects, thi...
Firefly algorithm (FA) has widely used to solve various complex optimization problems. However, FA has significant drawbacks in slow convergence rate and easily trapped into local optimum. To tackle these defects, this paper proposes an improved FA combined with extremal optimization (EO), named IFA-EO, where three strategies are incorporated. First, to balance tradeoff between exploration and exploitation, we adopt a new attraction model for FA operation, which combines the full attraction model and the single attraction model through the probability choice strategy. In single attraction model, inspired by the simulated annealing idea, small probability accepts the worse solution to improve the diversity of the offspring. Second, the adaptive step size is proposed according to the number of iterations. Third, we combine EO algorithm with powerful ability in local-search. IFA-EO is employed to handle three different parameters identification problems of photovoltaic model. For comparisons, we choose three swarm intelligence algorithms to compare with IFA-EO. Simulation results demonstrate the superiority of IFA-EO to other three competitors.
The strong unforgeability of digital signature means that no attacker can forge a valid signature on a message, even given some previous signatures on the message, which has been widely accepted as a common security r...
详细信息
With the growing maturity of the advanced edge-cloud collaboration and integrated sensing-communication-computing systems, edge intelligence has been envisioned as one of the enabling technologies for ubiquitous and l...
With the growing maturity of the advanced edge-cloud collaboration and integrated sensing-communication-computing systems, edge intelligence has been envisioned as one of the enabling technologies for ubiquitous and latency-sensitive machine learning based services in future wireless
As one of the evolutionary algorithms, firefly algorithm (FA) has been widely used to solve various complex optimization problems. However, FA has significant drawbacks in slow convergence rate and is easily trapped i...
详细信息
Multimodal sentiment analysis has become a popular research topic in recent years. However, existing methods have two unaddressed limitations: (1) they use limited supervised labels to train models, which makes it imp...
详细信息
Multimodal sentiment analysis has become a popular research topic in recent years. However, existing methods have two unaddressed limitations: (1) they use limited supervised labels to train models, which makes it impossible for model to fully learn sentiments in different modal data; (2) they employ text and image pre-trained models trained in different unimodal tasks to extract different modal features, so that the extracted features cannot take into account the interactive information between image and text. To solve these problems, in this paper we propose a Vision-Language Contrastive Learning network (VLCLNet). First, we introduce a pre-trained Large Language Model (LLM), which is trained from vast quantities of multimodal data, has better understanding ability for image and text contents, thus being effectively applied to different tasks while requiring few amount of labelled training data. Second, we adapt a Multimodal Large Language Model (MLLM), BLIP-2 (Bootstrapping Language-Image Pre-training) network, to extract multimodal fusion feature. Such MLLM can fully consider the correlation between images and texts when extracting features. In addition, due to the discrepancy between the pre-training task and the sentiment analysis task, the pre-trained model will output the suboptimal prediction results. We use LoRA (Low-Rank Adaptation) fine-tuning strategy to update the model parameters on sentiment analysis task, which avoids the issue of inconsistent task between pre-training task and downstream task. Experiments verify that the proposed VLCLNet is superior to other strong baselines.
暂无评论