Evaluating Feature Selection Methods for Short-Term Load Forecasting

Pirbazari, Aida Mehdipour; Chakravorty, Antorweep; Rong, Chunming

doi:10.1109/bigcomp.2019.8679188

Cited by 32 publications

(25 citation statements)

References 18 publications

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“…et al [23] proposed a model based on random forests for short term load forecast with special attention to load profile, holidays and customer behavior. Similarly, researchers in [7,24] utilized environmental and calendar features to develop a method for electric load forecasting based on Decision Tree and algorithms.…”

Section: Related Workmentioning

confidence: 99%

“…Load forecasting is also performed in various aggregation levels when it is applied to the areas with different geographical scales such as a country, city, small communities or a building. The forecasting task becomes more challenging when it comes to lower aggregation levels such as a building level since, many fluctuating factors affect a building's energy consumption with varying degrees such as weather parameters, building properties, Heating, Ventilating and Air-Conditioning (HAVC) facilities and the consumption behavior of occupants [6,7].…”

Section: Introductionmentioning

confidence: 99%

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Short-Term Load Forecasting Using Smart Meter Data: A Generalization Analysis

et al. 2020

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Short-term load forecasting ensures the efficient operation of power systems besides affording continuous power supply for energy consumers. Smart meters that are capable of providing detailed information on buildings energy consumption, open several doors of opportunity to short-term load forecasting at the individual building level. In the current paper, four machine learning methods have been employed to forecast the daily peak and hourly energy consumption of domestic buildings. The utilized models depend merely on buildings historical energy consumption and are evaluated on the profiles that were not previously trained on. It is evident that developing data-driven models lacking external information such as weather and building data are of great importance under the situations that the access to such information is limited or the computational procedures are costly. Moreover, the performance evaluation of the models on separated house profiles determines their generalization ability for unseen consumption profiles. The conducted experiments on the smart meter data of several UK houses demonstrated that if the models are fed with sufficient historical data, they can be generalized to a satisfactory level and produce quite accurate results even if they only use past consumption values as the predictor variables. Furthermore, among the four applied models, the ones based on deep learning and ensemble techniques, display better performance in predicting daily peak load consumption than those of others.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Short-Term Load Forecasting Using Smart Meter Data: A Generalization Analysis

et al. 2020

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“…In the energy forecasting literature, the lasso (least selection and shrinkage operator) seems to be a popular choice for shrinkage and feature selection methods in linear models; see, e.g., [15][16][17][18]. An extension of the lasso is given by the elastic net, which also has been applied [19][20][21][22][23][24][25].…”

Section: Estimation Of Proposed Nowcasting Modelmentioning

confidence: 99%

Load Nowcasting: Predicting Actuals with Limited Data

Ziel

2020

Energies

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We introduce the problem of load nowcasting to the energy forecasting literature. The recent load of the objective area is predicted based on limited available metering data within this area. Thus, slightly different from load forecasting, we are predicting the recent past using limited available metering data from the supply side of the system. Next, to an industry benchmark model, we introduce multiple high-dimensional models for providing more accurate predictions. They evaluate metered interconnector and generation unit data of different types like wind and solar power, storages, and nuclear and fossil power plants. Additionally, we augment the model by seasonal and autoregressive components to improve the nowcasting performance. We consider multiple estimation techniques based on the lassoand ridge and study the impact of the choice of the training/calibration period. The methodology is applied to a European TSO dataset from 2014 to 2019. The overall results show that in comparison to the industry benchmark, an accuracy improvement in terms of MAE and RMSE of about 60% is achieved. The best model is based on the ridge estimator and uses a specific non-standard shrinkage target. Due to the linear model structure, we can easily interpret the model output.

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“…Pirbazari et al investigated the various feature selection techniques of a household load prediction problem by means of different clusters of load profiles. They found out that there was a significant relationship between the model accuracy and household load profile volatility [39].…”

Section: The Importance Of Smart Grids In Developing Smart Citiesmentioning

confidence: 99%

A Widespread Review of Smart Grids Towards Smart Cities

et al. 2019

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Nowadays, the importance of energy management and optimization by means of smart devices has arisen as an important issue. On the other hand, the intelligent application of smart devices stands as a key element in establishing smart cities, which have been suggested as the solution to complicated future urbanization difficulties in coming years. Considering the scarcity of traditional fossil fuels in the near future, besides their ecological problems the new smart grids have demonstrated the potential to merge the non-renewable and renewable energy resources into each other leading to the reduction of environmental problems and optimizing operating costs. The current paper clarifies the importance of smart grids in launching smart cities by reviewing the advancement of micro/nano grids, applications of renewable energies, energy-storage technologies, smart water grids in smart cities. Additionally a review of the major European smart city projects has been carried out. These will offer a wider vision for researchers in the operation, monitoring, control and audit of smart-grid systems.

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Evaluating Feature Selection Methods for Short-Term Load Forecasting

Cited by 32 publications

References 18 publications

Short-Term Load Forecasting Using Smart Meter Data: A Generalization Analysis

Short-Term Load Forecasting Using Smart Meter Data: A Generalization Analysis

Load Nowcasting: Predicting Actuals with Limited Data

A Widespread Review of Smart Grids Towards Smart Cities

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