A novel fuzzy-based ensemble model for load forecasting using hybrid deep neural networks

Sideratos, G.; Ikonomopoulos, A.; Hatziargyriou, Nikos D.

doi:10.1016/j.epsr.2019.106025

Cited by 135 publications

(49 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Moreover, the fusion of different DNNs and group prediction can cooperate with each other, which can effectively improve the prediction accuracy. In [17], an innovative neural network architecture consisting of a radial basis function, a convolution, a pooling, and two fully connected layers is proposed and used in load forecasting.…”

Section: Introductionmentioning

confidence: 99%

Incorporating Load Fluctuation in Feature Importance Profile Clustering for Day-Ahead Aggregated Residential Load Forecasting

Huang

Wang

Sining³

et al. 2020

IEEE Access

View full text Add to dashboard Cite

Residents clustering in different periods of load fluctuation and aggregated forecasting can increase the load prediction accuracy. But the strength of load fluctuation reflects the difference in the electricity consumption behavior of residents and affects the cluster results of residents. This paper presents a new day-ahead aggregated load-forecasting method for distribution networks based on the load fluctuation and feature importance (FI) profile clustering of residents. First, the input features are determined, the FI profile of residents is determined, and residents are clustered according to the FI profile. Then, the crow search algorithm is used to optimize the initial cluster centers for preventing the clustering results from falling into a local optimum. And the cluster verification index S_Dbw, the sum of the average scattering for the clusters and the inter-cluster density, is used to evaluate the cluster quality. The optimal clustering results of the aggregated load for different fluctuation periods are determined via statistical experiments. Finally, a random forest predictor based on ensemble learning is selected. According to the optimal clustering results in different fluctuation periods, a rolling forecasting model is constructed to realize day-ahead aggregated load forecasting in a residential distribution network. INDEX TERMS Aggregated load forecasting, feature importance, load fluctuation, crow search algorithm, S-Dbw, random forest.

show abstract

Section: Introductionmentioning

confidence: 99%

Incorporating Load Fluctuation in Feature Importance Profile Clustering for Day-Ahead Aggregated Residential Load Forecasting

Huang

Wang

Sining³

et al. 2020

IEEE Access

View full text Add to dashboard Cite

show abstract

“…The 80% load data is for training, and the remaining 20% is kept for testing and validation purposes, respectively. For validation, the FA-HELF framework is compared with benchmark frameworks like F-RBF-CNN [9], SDPSO-ELM [35], and SSA-SVM-CS [36] in terms of convergence speed and accuracy. These frameworks are selected because of their architectural resemblance with the proposed FA-HELF framework, which is needed for a fair comparison.…”

Section: Simulation Results and Discussionmentioning

confidence: 99%

“…In [8], a framework based on a multi-task regressor is proposed to predict electrical energy consumption based on the recorded energy consumption data by the smart meters. Sideratos et al [9] proposed a load prediction model based on a hybrid DNN (HDNN). The proposed HDNN based model utilizes key parameters of ANN and deep learning models to resolve the forecasting issues.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Approach for Energy Consumption Forecasting With a New Feature Engineering and Optimization Framework in Smart Grid

et al. 2020

View full text Add to dashboard Cite

Electric energy consumption forecasting enables distribution system operators to perform efficient energy management by flexibly engaging energy consumers under the intelligent demand-response program in the smart grid (SG). With this motivation, in this paper, a fast and accurate hybrid electrical energy forecasting (FA-HELF) framework is developed. The proposed framework integrates two modules with support vector machine (SVM) based forecaster. These modules are data pre-processing and feature engineering, and modified enhanced differential evolution (mEDE) based optimizer. First, feature selection algorithms like random forests and relief-F are combined to devise a hybrid feature selection algorithm to alleviate redundancy. Secondly, for feature extraction, a radial basis Kernel-based principal component analysis algorithm is employed to eliminate the dimensionality reduction problem. Finally, to conduct accurate and fast electrical energy consumption forecasting, the mEDE based optimizer is integrated with the SVM based forecaster. The resulting FA-HELF framework is tested on publicly available independent system operator New England (ISO-NE) control area hourly load data. The results demonstrate that the FA-HELF framework is robust and shows significant improvements when compared to other benchmark frameworks in terms of accuracy and convergence speed. INDEX TERMS Electrical energy consumption forecasting, energy management, smart grid, grey correlation analysis, differential evolution, radial basis kernel-based principal component analysis, support vector machine.

show abstract

“…The LSTM's forecasting mechanism has been widely used for many time-series forecasting in recent years [33]. So, we used the LSTM model which is suitable for time-series forecasting, but we made it possible to reflect the weight function value used in the existing MIDAS to make the model, considering the power demand's volatility.…”

Section: Lstm-based Power Demand Forecasting Modelmentioning

confidence: 99%

Power Demand Forecasting using Long Short-Term Memory (LSTM) Deep-Learning Model for Monitoring Energy Sustainability

2020

View full text Add to dashboard Cite

The purpose of this study is to design a novel custom power demand forecasting algorithm based on the LSTM Deep-Learning method regarding the recent power demand patterns. We performed tests to verify the error rates of the forecasting module, and to confirm the sudden change of power patterns in the actual power demand monitoring system. We collected the power usage data in every five-minute resolution in a day from some groups of the residential, public offices, hospitals, and industrial factories buildings in one year. In order to grasp the external factors and to predict the power demand of each facility, a comparative experiment was conducted in three ways; short-term, long-term, seasonal forecasting exp[eriments. The seasonal patterns of power demand usages were analyzed regarding the residential building. The overall error rates of power demand forecasting using the proposed LSTM module were reduced in terms of each facility. The predicted power demand data shows a certain pattern according to each facility. Especially, the forecasting difference of the residential seasonal forecasting pattern in summer and winter was very different from other seasons. It is possible to reduce unnecessary demand management costs by the designed accurate forecasting method.

show abstract

A novel fuzzy-based ensemble model for load forecasting using hybrid deep neural networks

Cited by 135 publications

References 37 publications

Incorporating Load Fluctuation in Feature Importance Profile Clustering for Day-Ahead Aggregated Residential Load Forecasting

Incorporating Load Fluctuation in Feature Importance Profile Clustering for Day-Ahead Aggregated Residential Load Forecasting

A Hybrid Approach for Energy Consumption Forecasting With a New Feature Engineering and Optimization Framework in Smart Grid

Power Demand Forecasting using Long Short-Term Memory (LSTM) Deep-Learning Model for Monitoring Energy Sustainability

Contact Info

Product

Resources

About