Hybrid Load Forecasting for Mixed-Use Complex Based on the Characteristic Load Decomposition by Pilot Signals

Park, Kanggu; Yoon, Seungwook; Hwang, Euiseok

doi:10.1109/access.2019.2892475

Cited by 51 publications

(20 citation statements)

References 27 publications

(33 reference statements)

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“…With the large-scale popularization of smart meters [4], the high-resolution electricity load data recorded by smart meters can break the restrictions of the physical structure of traditional power system measurement and improve the accuracy of load forecasting [5]. Accurate aggregated day-ahead load forecasting for a distribution network is important for the economic and safe operation [6] of the distribution system.…”

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

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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.

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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

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“…However, as suggested by [12,28,29], in order to show our proposed model significance over the other models, both the Wilcoxon signed rank test [30] and Friedman test [31] are conducted using all the models' forecasting error given input from those testing datasets. The Wilcoxon signed rank test will compare the W statistic with the Wilcoxon critical value W which are expressed in Equations (18) and (19) (for huge number of data), respectively.…”

Section: Discussionmentioning

confidence: 99%

“…Unfortunately, these traditional models only provide good accuracy if the electrical load and other parameters have a linear relationship. Meanwhile, the advanced model is a data-driven model implementing the machine learning technique for instance support vector machine (SVM) [10], K-nearest neighbor (KNN) [11], and others [12][13][14].However, based on the recent publications [15][16][17][18], the deep learning-based methods show the most convincing performance by outperforming other machine learning-based solutions. The main reason of the deep learning superiority is first, deep learning does not highly rely on feature engineering and the hyperparameters tuning is relatively easier compared to other data-driven models.…”

mentioning

confidence: 99%

“…However, based on the recent publications [15][16][17][18], the deep learning-based methods show the most convincing performance by outperforming other machine learning-based solutions. The main reason of the deep learning superiority is first, deep learning does not highly rely on feature engineering and the hyperparameters tuning is relatively easier compared to other data-driven models.…”

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confidence: 99%

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Deep Learning-Based Short-Term Load Forecasting for Supporting Demand Response Program in Hybrid Energy System

et al. 2019

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A novel method for short-term load forecasting (STLF) is proposed in this paper. The method utilizes both long and short data sequences which are fed to a wavenet based model that employs dilated causal residual convolutional neural network (CNN) and long short-term memory (LSTM) layer respectively to hourly forecast future load demand. This model is aimed to support the demand response program in hybrid energy systems, especially systems using renewable and fossil sources. In order to prove the generality of our model, two different datasets are used which are the ENTSO-E (European Network of Transmission System Operators for Electricity) dataset and ISO-NE (Independent System Operator New England) dataset. Moreover, two different ways of model testing are conducted. The first is testing with the dataset having identical distribution with validation data, while the second is testing with data having unknown distribution. The result shows that our proposed model outperforms other deep learning-based model in terms of root mean squared error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). In detail, our model achieves RMSE, MAE, and MAPE equal to 203.23, 142.23, and 2.02 for the ENTSO-E testing dataset 1 and 292.07, 196.95 and 3.1 for ENTSO-E dataset 2. Meanwhile, in the ISO-NE dataset, the RMSE, MAE, and MAPE equal to 85.12, 58.96, and 0.4 for ISO-NE testing dataset 1 and 85.31, 62.23, and 0.46 for ISO-NE dataset 2. Energies 2019, 12, 3359 2 of 16users' load demand. The major concern of this scheme is the cost to be borne by users once the renewable sources cannot supply adequate power to the grid in which they will pay an expensive fossil-based electric price. Moreover, fossil energy tends to gradually increase leading to economic conflict in society [2]. Therefore, in order to tackle this issue, an appropriate demand response scheme can be applied.Demand response is the change of electric usage by users due to change of electric price or maybe an incentive as a reward of lowering their power consumption [3]. Applying demand response to this hybrid system is very beneficial for shaving peak load demand [4,5], leading to the reduction of fossil energy consumption. Moreover, it can provide short-term impact and economic benefit for both consumer and utility.In order to support this demand response, short-term load forecasting (STLF) is very important for predicting whether the energy storage from renewable sources is able to handle the forthcoming power consumption or not. If the prediction states that the storage is not adequate to support the future load, then the electricity utility can announce this situation to the users, which eventually triggers them to reduce their electric usage, because users do not only want to pay more for conventional energy source but also want to get incentives from the authorities.Fortunately, with the help of developed infrastructures like smart meters equipped with a lot of sensors and the Internet of Things (IoT), a robust STLF method is feasible to...

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“…Improving the reliability of renewable energy generation by fault detection and diagnosis (FDD) and correcting faulty data is essential for maintaining the efficiency of PV generation [5]. In addition, reliable information is required to be applied for various power applications, e.g., energy scheduling [6] and energy forecasting [7,8], to guarantee safe and stable grid systems.…”

Section: Introductionmentioning

confidence: 99%

Clustering-Based Self-Imputation of Unlabeled Fault Data in a Fleet of Photovoltaic Generation Systems

Park

Kim

et al. 2020

Energies

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This work proposes a fault detection and imputation scheme for a fleet of small-scale photovoltaic (PV) systems, where the captured data includes unlabeled faults. On-site meteorological information, such as solar irradiance, is helpful for monitoring PV systems. However, collecting this type of weather data at every station is not feasible for a fleet owing to the limitation of installation costs. In this study, to monitor a PV fleet efficiently, neighboring PV generation profiles were utilized for fault detection and imputation, as well as solar irradiance. For fault detection from unlabeled raw PV data, K-means clustering was employed to detect abnormal patterns based on customized input features, which were extracted from the fleet PVs and weather data. When a profile was determined to have an abnormal pattern, imputation for the corresponding data was implemented using the subset of neighboring PV data clustered as normal. For evaluation, the effectiveness of neighboring PV information was investigated using the actual rooftop PV power generation data measured at several locations in the Gwangju Institute of Science and Technology (GIST) campus. The results indicate that neighboring PV profiles improve the fault detection capability and the imputation accuracy. For fault detection, clustering-based schemes provided error rates of 0.0126 and 0.0223, respectively, with and without neighboring PV data, whereas the conventional prediction-based approach showed an error rate of 0.0753. For imputation, estimation accuracy was significantly improved by leveraging the labels of fault detection in the proposed scheme, as much as 18.32% reduction in normalized root mean square error (NRMSE) compared with the conventional scheme without fault consideration.

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Hybrid Load Forecasting for Mixed-Use Complex Based on the Characteristic Load Decomposition by Pilot Signals

Cited by 51 publications

References 27 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

Deep Learning-Based Short-Term Load Forecasting for Supporting Demand Response Program in Hybrid Energy System

Clustering-Based Self-Imputation of Unlabeled Fault Data in a Fleet of Photovoltaic Generation Systems

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