An improved load forecast model using factor analysis: An Australian case study

Chen, Xi; Qiu, Jing; Zhang, Dong

doi:10.1109/icinfa.2017.8079031

Cited by 6 publications

(2 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It has become an important part of modern energy management systems (EMS) [1][2][3]. However, the short-term power load is affected by many complex factors [4][5][6][7] such as geographical location, weather conditions, seasonal effect etc. so that it has the characteristics of high fluctuation and uncertainty.…”

Section: Introductionmentioning

confidence: 99%

A decomposition‐based multi‐time dimension long short‐term memory model for short‐term electric load forecasting

Huang

Zhou

et al. 2021

IET Generation Trans & Dist

View full text Add to dashboard Cite

Short‐term load forecasting is essential to power systems management. However, most existing forecasting methods fail to fully consider how to rationally integrate the intrinsic time‐related dimensions of electric load data and the decomposition methods into machine learning models so that their prediction accuracy and robustness still have much room for improvement. To solve this problem, this paper proposes a decomposition‐based multi‐time dimension long short‐term memory (DB‐MTD‐LSTM) model for short‐term electric load forecasting (STELF). In DB‐MTD‐LSTM, empirical mode decomposition with adaptive noise (CEEMDAN) is first introduced to smooth non‐linear non‐stationary electric load data and constrain the modal aliasing or noise of decomposed electric load data in the traditional decomposed method. A joint relevant time dimensions method (JRTDM) is then developed using autocorrelation analysis to rationally extract the temporal characteristics of decomposed data in multiple time dimensions. An improved LSTM called MTD‐LSTM is developed by combining JRTDM with LSTM, which can effectively apply multi‐dimensional time characteristics of the decomposed load to improve the predictive accuracy and robustness. Several datasets from Australia and China are performed to check the predictive performance of DB‐MTD‐LSTM. Experimental results verify that DB‐MTD‐LSTM has better predictive accuracy and satisfactory robustness compared with state‐of‐the‐art and conventional predictive models.

show abstract

Section: Introductionmentioning

confidence: 99%

A decomposition‐based multi‐time dimension long short‐term memory model for short‐term electric load forecasting

Huang

Zhou

et al. 2021

IET Generation Trans & Dist

View full text Add to dashboard Cite

show abstract

“…However, current research on the effect of policy implementation is mostly based on qualitative analyses, and research on quantitative measurement of energy policy is still lacking. Existing methods [6][7][8] do not take into account the complex non-linear relationship between electricity consumption and policy factors; therefore, they cannot precisely quantify the impact of policy macro-control on regional electricity consumption.…”

Section: Introductionmentioning

confidence: 99%

The Economy and Policy Incorporated Computing System for Social Energy and Power Consumption Analysis

et al. 2021

View full text Add to dashboard Cite

Human activities, such as energy consumption and economic development, will significantly affect the natural environment, while changes in the natural environment will also affect the sustainability of human society. Studying the energy consumption changes of human society and forecasting medium and long-term electricity demand will help realize the sustainable development of energy in future society. However, current medium- and long-term electricity consumption forecasts have insufficient data samples and the inability to consider policy impacts. Here, we develop an Economy and Policy Incorporated Computing System (EPICS), which can use artificial intelligence technology to extract the summaries of energy policy texts automatically and calculate the importance index of energy policy. It can also process economic data of different lengths to expand samples of medium- and long-term electricity consumption forecasting effectively. A forecasting method that considers policy factors and mixed-frequency economic data is introduced to estimate future social energy and power consumption. This method has shown good forecasting ability in 27 months. The effect of EPICS can be demonstrated by predicting the medium- and long-term electricity demand.

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

An improved load forecast model using factor analysis: An Australian case study

Cited by 6 publications

References 20 publications

A decomposition‐based multi‐time dimension long short‐term memory model for short‐term electric load forecasting

A decomposition‐based multi‐time dimension long short‐term memory model for short‐term electric load forecasting

The Economy and Policy Incorporated Computing System for Social Energy and Power Consumption Analysis

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

Contact Info

Product

Resources

About