Hybrid filter–wrapper feature selection for short-term load forecasting

2015 ◽  
Vol 40 ◽  
pp. 17-27 ◽  
Author(s):  
Zhongyi Hu ◽  
Yukun Bao ◽  
Tao Xiong ◽  
Raymond Chiong
Keyword(s):  
Feature Selection ◽  
Load Forecasting ◽  
Hybrid Filter ◽  
Short Term ◽  
Short Term Load Forecasting ◽  
Selection For ◽  
Wrapper Feature Selection

An intelligent approach towards very short-term load forecasting

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Uttamarani Pati ◽  
Papia Ray ◽  
Arvind R. Singh
Keyword(s):  
Genetic Algorithm ◽  
Feature Selection ◽  
Load Forecasting ◽  
Dew Point ◽  
Short Term ◽  
Spinning Reserve ◽  
Feature Selection Technique ◽  
Selection Technique ◽  
Load Demand ◽  
Short Term Load Forecasting

Abstract Very short term load forecasting (VSTLF) plays a pivotal role in helping the utility workers make proper decisions regarding generation scheduling, size of spinning reserve, and maintaining equilibrium between the power generated by the utility to fulfil the load demand. However, the development of an effective VSTLF model is challenging in gathering noisy real-time data and complicates features found in load demand variations from time to time. A hybrid approach for VSTLF using an incomplete fuzzy decision system (IFDS) combined with a genetic algorithm (GA) based feature selection technique for load forecasting in an hour ahead format is proposed in this research work. This proposed work aims to determine the load features and eliminate redundant features to form a less complex forecasting model. The proposed method considers the time of the day, temperature, humidity, and dew point as inputs and generates output as forecasted load. The input data and historical load data are collected from the Northern Regional Load Dispatch Centre (NRLDC) New Delhi for December 2009, January 2010 and February 2010. For validation of proposed method efficacy, it’s performance is further compared with other conventional AI techniques like ANN and ANFIS, which are integrated with genetic algorithm-based feature selection technique to boost their performance. These techniques’ accuracy is tested through their mean absolute percentage error (MAPE) and normalized root mean square error (nRMSE) value. Compared to other conventional AI techniques and other methods provided through previous studies, the proposed method is found to have acceptable accuracy for 1 h ahead of electrical load forecasting.

A Novel Quantum Neural Network Model with Variable Selection for Short Term Load Forecasting

2010 ◽  
Vol 20-23 ◽  
pp. 612-617 ◽  
Author(s):  
Wei Sun ◽  
Yu Jun He ◽  
Ming Meng
Keyword(s):  
Neural Network ◽  
Variable Selection ◽  
Network Model ◽  
Neural Network Model ◽  
Load Forecasting ◽  
Short Term ◽  
Bp Network ◽  
Quantum Neural Network ◽  
Short Term Load Forecasting ◽  
Selection For

The paper presents a novel quantum neural network (QNN) model with variable selection for short term load forecasting. In the proposed QNN model, first, the combiniation of maximum conditonal entropy theory and principal component analysis method is used to select main influential factors with maximum correlation degree to power load index, thus getting effective input variables set. Then the quantum neural network forecating model is constructed. The proposed QNN forecastig model is tested for certain province load data. The experiments and the performance with QNN neural network model are given, and the results showed the method could provide a satisfactory improvement of the forecasting accuracy compared with traditional BP network model.

scholarly journals Accurate Forecast Improvement Approach for Short Term Load Forecasting Using Hybrid Filter-Wrap Feature Selection

2019 ◽  
Vol 5 (2) ◽  
pp. 37-49
Author(s):  
Samuel Atuahene ◽  
Yukun Bao ◽  
Patricia Semwaah Gyan ◽  
Yao Yevenyo Ziggah
Keyword(s):  
Energy Demand ◽  
Load Forecasting ◽  
Hybrid Filter ◽  
Short Term ◽  
Input Selection ◽  
Model Free ◽  
Short Term Load Forecasting ◽  
Accurate Forecast ◽  
Planning Technique ◽  
Primary Composition

Accurate hybrid filter– wrap approach is quite important for short term load forecasting as it not only improve forecasting accuracy performance, but also could effectively avoid converging prematurely. The importance of input selection-features is an essential part to develop models. Currently and dynamic surroundings, energy demand, quantity and values are becoming unpredictable and progressively volatile. Increasing amount of decision-making procedures in the industries in terms of energy require a wide-ranging outlook of the uncertain forthcoming. This paper explains the selection method for the proposed hybrid filter-wrapper whose primary composition includes Personal Modular Impactor (PMI) based filter technique and the Firefly Algorithm (FA) based filter wrapper. The filter wrapper planning technique involves the selection of the best corresponding inputs by a predefined model-free technique that measures the specific relationship between the output selection and the input variable. FA wrapper based technique is more useful compared to the filter procedure. Modular Impactor (MI) is a technique mostly preferred by individuals to measure the dependency of variables and commonly used to select input features and in other key fields.

User-Level Ultra-Short-Term Load Forecasting Model Based on Optimal Feature Selection and Bahdanau Attention Mechanism

2021 ◽  
pp. 2150279
Author(s):  
Ziyao Wang ◽  
Huaqiang Li ◽  
Zizhuo Tang ◽  
Yang Liu
Keyword(s):  
Feature Selection ◽  
Power Consumption ◽  
Electric Power ◽  
Load Forecasting ◽  
Forecasting Model ◽  
Selection Algorithm ◽  
Feature Selection Algorithm ◽  
Short Term ◽  
Short Term Load Forecasting ◽  
Optimal Feature

Accurate ultra-short-term load forecasting is of great significance for real-time power generation scheduling and development of power cyber physical systems (Power CPS). However, in order to forecast the future load using the current high-dimensional, diverse and heterogeneous electric power consumption information, new challenges have been raised to the effective feature selection and the accurate load forecasting algorithms. However, very limited existing works consider the feature selection for the electric power consumption information and impacts to the thereafter load forecasting model. In view of this point, features that are critical to the load forecasting are selected using an embedded feature selection algorithm based on LightGBM to form an optimal feature set, with which a sequence to sequence (S2S) and gated recurrent unit (GRU)-based ultra-short-term load forecasting model that incorporates Bahdanau attention (BA) mechanism is presented. The S2S-GRU model is based on an encoding–decoding framework that is compatible to the input and output data series with variable lengths. By introducing the BA mechanism, loss of previous information issue of GRU can be solved. Experimental results show that first the presented feature selection algorithm can help to improve the performance of the load forecasting model. Second, the presented load forecasting model can find a compromise between the forecasting efficiency and accuracy.

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