Study on power consumption load forecast based on K-means clustering and FCM–BP model

Bian, Haihong; Yiqun, Zhong; Sun, Jianshuo; Shi, Fangchu

doi:10.1016/j.egyr.2020.11.148

Cited by 60 publications

(31 citation statements)

References 2 publications

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“…Conventional clustering methods such as the K-means clustering algorithm, due to its strong universality and simple principle, are widely used in the field of clustering, but they only classify samples simply, and classifying samples is not accurate. The effect is not good when the sample is not engaged, so in order to improve the accuracy of the prediction model proposed in this paper, the FCM (Bian et al, 2020) value based on the membership degree is used. The clustering method selects similar days, and the specific selection steps are as follows:…”

Section: Similar Daily Clusters Based On Fcmmentioning

confidence: 99%

Short-Term Forecasting and Uncertainty Analysis of Photovoltaic Power Based on the FCM-WOA-BILSTM Model

Cao

Zhou

et al. 2022

Front. Energy Res.

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Aiming to solve the problem that photovoltaic power generation is always accompanied by uncertainty and the short-term prediction accuracy of photovoltaic power (PV) is not high, this paper proposes a method for short-term photovoltaic power forecasting (PPF) and uncertainty analysis using the fuzzy-c-means (FCM), whale optimization algorithm (WOA), bi-directional long short-term memory (BILSTM), and no-parametric kernel density estimation (NPKDE). First, the principal component analysis (PCA) is used to reduce the dimensionality of the daily feature vector, and then the FCM is used to divide the weather into four categories: sunny, cloudy, rainy, and extreme weather. Second, the WOA algorithm is used to train the hyperparameters of BILSTM, and finally, the optimized hyperparameters were used to construct a WOA-BILSTM prediction model to train the four types of weather samples after FCM clustering. The NPKDE method was used to calculate the probability density distribution of PV prediction errors and confidence intervals for PPF. The RMSEs of the FCM-WOA-BILSTM model are 2.46%, 4.89%, and 1.14% for sunny, cloudy, and rainy weather types, respectively. The simulation results of the calculation example show that compared with the BP, LSTM, GRU, PSO-BILSTM, and FCM-PSO-BP models, the proposed FCM-WOA-BILSTM model has higher prediction accuracy under various weather types, which verifies the effectiveness of the method. Moreover, the NPKDE method can accurately describe the probability density distribution of forecast errors.

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Section: Similar Daily Clusters Based On Fcmmentioning

confidence: 99%

Short-Term Forecasting and Uncertainty Analysis of Photovoltaic Power Based on the FCM-WOA-BILSTM Model

Cao

Zhou

et al. 2022

Front. Energy Res.

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“…Such aggregated models solely use previously observed aggregated demand as input and do not leverage fine-resolution smart-meter data [15], [16], [23], [24]. More recently, cluster-based approaches (CBAs) have been used as an alternative to aggregated models to leverage fine-resolution data [25]- [32]. CBAs involve grouping smart-meter data into clusters and developing separate prediction models for every cluster.…”

Section: Introductionmentioning

confidence: 99%

Smart-Meter Big Data for Load Forecasting: An Alternative Approach to Clustering

et al. 2022

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Accurate forecasting of electricity demand is vital to the resilient management of energy systems. Recent efforts in harnessing smart-meter data to improve forecasting accuracy have primarily centered around cluster-based approaches (CBAs), where smart-meter data are grouped into a small number of clusters and separate prediction models are developed for each cluster. The cluster-based predictions are then aggregated to compute the total demand. CBAs have provided promising results compared to conventional approaches that are generally not conducive to integrating smart-meter data. However, CBAs are computationally costly and suffer from the curse of dimensionality, especially under scenarios involving smart-meter data from millions of customers. In this work, we propose an efficient reduced model approach (RMA) that leverages a novel hierarchical dimension reduction algorithm to enable the integration of fine-resolution high-dimensional smart-meter data for millions of customers in load prediction. We demonstrate the applicability of our proposed approach by using data from a utility company, based in Illinois, United States, with more than 3.7 million customers and present model performance in-terms of forecast accuracy. The proposed hierarchical dimension reduction approach enables utilizing the high-resolution data from smart-meters in a scalable manner that is not exploitable otherwise. The results shows significant improvements in forecast accuracy compared to the available approaches that either do not harness fine-resolution data or are not scalable to large-scale smart-meter big data.INDEX TERMS Short-term load forecasting, smart-meter data, big data, hierarchical dimension reduction.

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“…The concept of FCMs has attracted special attention in recent years as a powerful tool to manipulate knowledge by imitating human reasoning and thinking. For example, a study [4] proposes a short-term power load forecasting model based on K-means and FCM-BP. There are two ways to construct FCMs: artificial methods and computational methods.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Cognitive Maps Based on D-Number Theory

Shao

2022

IEEE Access

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In real life, there will be a lot of uncertainty problems, one of which is due to the vagueness of the concept of things, that is, it is difficult to determine whether an object conforms to this concept. This situation widely exists in some states, phenomena, parameters and interrelationships between things. For such uncertain events with heavy subjective influencing factors and incomplete data, it is suitable to use fuzzy methods to deal with them. In present our work, we firstly introduce the notion of D-number cognitive maps (DCMs), which are intelligent framework models based on D-number theory and cognitive maps. Compared with Evidential Cognitive Maps (ECMs) and Fuzzy Cognitive Maps (FCMs), DCMs can fuse multiple sources of information with uncertainty and construct a cognitive map model with incomplete and conflicting information. To better solve the problem of knowledge combination, D-number fuzzy cognitive maps (DFCMs) are also constructed based on D-number theory and fuzzy cognitive maps. In many practical applications, the establishment of fuzzy cognitive maps is usually completed by using a simple arithmetic average method for multiple experts to obtain comprehensive fuzzy cognitive maps. This simple processing method may lead to the loss of some important information so that the synthesized results do not reflect reality. To overcome this challenge, we synthesize the knowledge of multiple experts by using D-number theory and the characteristics of the representation of expert knowledge in FCMs. It is based on expert knowledge, and the synthesized reliability distribution function is used as the basis of final weight synthesis.

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Study on power consumption load forecast based on K-means clustering and FCM–BP model

Cited by 60 publications

References 2 publications

Short-Term Forecasting and Uncertainty Analysis of Photovoltaic Power Based on the FCM-WOA-BILSTM Model

Short-Term Forecasting and Uncertainty Analysis of Photovoltaic Power Based on the FCM-WOA-BILSTM Model

Smart-Meter Big Data for Load Forecasting: An Alternative Approach to Clustering

Fuzzy Cognitive Maps Based on D-Number Theory

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