Modular Predictor for Day-Ahead Load Forecasting and Feature Selection for Different Hours

Lin, Lin; Lin, Xu; Hu, Zhiqiang; Huang, Nantian

doi:10.3390/en11071899

Cited by 8 publications

(2 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Other researchers introduced the latest neural network algorithm support vector machine into short-term power load forecasting, and compared its performance with the autoregressive model. The results show that the support vector machine is better than the autoregressive model that uses the same data to build and test the two models based on the root mean square error between the actual data and the predicted data [8]. At present, short-term power load forecasting research has become a key research content in the power field, and many results have been achieved in this regard, providing data support for the formulation of power grid planning schemes.…”

Section: Introductionmentioning

confidence: 99%

Short-term Load Forecasting System for Power System Based on Big Data

Yang

2021

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

Short-term load forecasting of power system is an important task of power distribution system. Accurate short-term load forecasting provides the best configuration for grid power generation and distribution, maximizing energy saving and ensuring stable operation. This paper aims to study the design of short-term load forecasting system of power system based on big data. On the basis of analyzing power system load forecasting algorithms, classification of load forecasting, characteristics of load forecasting and system design principles, each module of the system is designed in detail, and finally tested the performance of the system. The test results show that the system has no adverse reactions in the use of a large number of users and repeated operation for a long time. In the case of large throughput, the system has a satisfactory response time and relatively reliable system stability.

show abstract

Section: Introductionmentioning

confidence: 99%

Short-term Load Forecasting System for Power System Based on Big Data

Yang

2021

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

show abstract

“…Furthermore, data processing and data storage of each piece of the dataset are required because the resolution of time-series data is different. Therefore, recent research trends use the technique of feature selection [14,15] or decomposing the load profile to extract the characteristics of the load using signal processing theory [16][17][18][19][20][21][22][23][24].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Based on Multi-Decomposition for Short-Term Load Forecasting

et al. 2018

View full text Add to dashboard Cite

Load forecasting is a key issue for efficient real-time energy management in smart grids. To control the load using demand side management accurately, load forecasting should be predicted in the short term. With the advent of advanced measuring infrastructure, it is possible to measure energy consumption at sampling rates up to every 5 min and analyze the load profile of small-scale energy groups, such as individual buildings. This paper presents applications of deep learning using feature decomposition for improving the accuracy of load forecasting. The load profile is decomposed into a weekly load profile and then decomposed into intrinsic mode functions by variational mode decomposition to capture periodic features. Then, a long short-term memory network model is trained by three-dimensional input data with three-step regularization. Finally, the prediction results of all intrinsic mode functions are combined with advanced measuring infrastructure measured in the previous steps to determine an aggregated output for load forecasting. The results are validated by applications to real-world data from smart buildings, and the performance of the proposed approach is assessed by comparing the predicted results with those of conventional methods, nonlinear autoregressive networks with exogenous inputs, and long short-term memory network-based feature decomposition.

show abstract

Review of load data analytics using deep learning in smart grids: Open load datasets, methodologies, and application challenges

Elahe

Jin

Zeng

2021

Intl J of Energy Research

View full text Add to dashboard Cite

The collection and storage of large-scale load data in a smart grid provide new approaches for the efficient, economical, and safe operation of power systems.Deep Learning (DL) has become increasingly popular for large-scale load data analytics in recent years because of its ability to extract latent features and discovering complex relationships. This paper first overviews eight typical open load datasets of the grid and smart meter collected worldwide, the challenges faced by conventional machine learning, and the DL techniques applied to these challenges. A comprehensive review of the applications of DL techniques is then conducted from the perspective of analysis, forecast, management, and presented observation on each application. Critical points of DL models for improving performance are further discussed. In conclusion, several pressing problems of DL in load data analytics are identified, such as the accuracy gap between the actual and the expected, the generalization of hyperparameter setting, and the interpretation mechanism of DL output, which need special attention.

show abstract

Modular Predictor for Day-Ahead Load Forecasting and Feature Selection for Different Hours

Cited by 8 publications

References 38 publications

Short-term Load Forecasting System for Power System Based on Big Data

Short-term Load Forecasting System for Power System Based on Big Data

Deep Learning Based on Multi-Decomposition for Short-Term Load Forecasting

Review of load data analytics using deep learning in smart grids: Open load datasets, methodologies, and application challenges

Contact Info

Product

Resources

About