Short Term Load Forecasting Based on iForest-LSTM

Ma, Yi; Zhang, Qian; Ding, Jinjin; Wang, Qiongjing; Ma, Jie

doi:10.1109/iciea.2019.8833755

Cited by 25 publications

(10 citation statements)

References 6 publications

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“…For promoting the forecasting accuracy, LSTM is also combined with other models to build mixed models. For example, Ma et al [9] propose an hourly load prediction model of regional energy Internet system based on ensemble empirical mode decomposition (EEMD) and LSTM algorithm. Because EEMD can decompose the original data and effectively avoid mode aliasing, and LSTM has a more accurate prediction effect, EEMD-LSTM can predict the power load more accurately.…”

Section: Related Workmentioning

confidence: 99%

Short-term Load Forecasting with Deep Learning Techniques

Dai

2023

J. Phys.: Conf. Ser.

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Short-term load forecasting is essential to the modern power system, which is the basis to guarantee the equilibrium of power supply and demand. It not only ensures the secure and stable operation of power grid, but also provides an important basis for power load dispatch, which can effectively reduce the economic loss caused by energy surplus or energy shortage in a short term. In the previous research work, traditional linear regression models, typical machine learning models and other models have been explored, but there are still problems of low accuracy or single influence factors. In this paper, a short-term forecasting model based on Temporal Convolutional Network (TCN) is proposed. Historical power load data, weather conditions, date information and their combinations are separately used to forecast the power load in different prediction models. Compared with other models, the evaluation metric of TCN model shows that its prediction accuracy is higher, and it is more suitable for short-term load forecasting.

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Section: Related Workmentioning

confidence: 99%

Short-term Load Forecasting with Deep Learning Techniques

Dai

2023

J. Phys.: Conf. Ser.

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“…This reduced the complexity of the power load sequence and improved the accuracy of load forecasting. The vanilla LSTM network was improved in [24] by cleaning and processing the raw load data using isolated forest algorithm.…”

Section: Timementioning

confidence: 99%

A Novel Technique for Short-Term Load Forecasting Using Sequential Models and Feature Engineering

et al. 2021

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With the advent of smart grid, load forecasting is emerging as an essential technology to implement optimal planning and control of grid assets. Ergo in recent years, a significant thrust can be witnessed for the research towards the improvement of the prediction of the energy demand. However thus far there has not been any one technique in the literature that is shown to give best forecasts for a variety of sites; almost all the papers published on load forecasting, report their best results on just one of the dataset. This problem accentuates further when the training data does not have enough data points to learn patterns over all the seasons. Hence to devise a load forecasting technique that can yield the best estimates on diverse datasets, specially when the training data is limited, is a big challenge, which is addressed in this paper. The paper presents a novel combination of deep learning with feature engineering for short-term load forecasting. The proposed architecture, named as Deep Derived Feature Fusion (DeepDeFF), is based on the sequential model in conjunction with the hand-crafted derived features in order to aid the model for better learning and predictions. The raw data and the hand-crafted features are trained at separate levels, then their respective outputs are combined to make the final prediction. The efficacy and robustness of the proposed methodology is evaluated on diverse datasets from five countries with completely different patterns. The results demonstrate that the proposed technique is superior to the existing state of the art.

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“…Ma et al [22] proposed short term load forecasting using isolation forest (iForest) and long short-term memory (LSTM) recurrent neural network. Isolation forest algorithm was intended towards prepossesses the historical data.…”

Section: Literature Surveymentioning

confidence: 99%

A Survey on Electric Power Demand Forecasting

Shivarkar

Malik

2021

Recent Trends in Intensive Computing

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Recently there has been tremendous change in use of the forecasting techniques due to the increase in availability of the power generation systems and the consumption of the electricity by different utilities. In the field of power generation and consumption it is important to have the accurate forecasting model to avoid the different losses. With the current development in the era of smart grids, it integrates electric power generation, demand and the storage, which requires more accurate and precise demand and generation forecasting techniques. This paper relates the most relevant studies on electric power demand forecasting, and presents the different models. This paper proposes a novel approach using machine learning for electric power demand forecasting.

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Short Term Load Forecasting Based on iForest-LSTM

Cited by 25 publications

References 6 publications

Short-term Load Forecasting with Deep Learning Techniques

Short-term Load Forecasting with Deep Learning Techniques

A Novel Technique for Short-Term Load Forecasting Using Sequential Models and Feature Engineering

A Survey on Electric Power Demand Forecasting

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