LSTM load forecasting algorithm based on time-sharing somatosensory

Xu, Shuo; Mo, Wenxiong; Le, Luan Thanh; Tong, Rui; Lan, Tian

doi:10.1049/icp.2021.2347

Cited by 3 publications

(5 citation statements)

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“…The somatosensory temperature is different from the temperature measured by the meteorological station, and the change is more complicated because of the comprehensive influence of air temperature, air humidity, wind speed, and other conditions [21].…”

Section: Composite Meteorological Factormentioning

confidence: 99%

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Prediction of Daily Water Consumption in Residential Areas Based on Meteorologic Conditions—Applying Gradient Boosting Regression Tree Algorithm

Li,

Peng,

Zheng

et al. 2023

Water

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A more accurate way of water consumption forecasting can be used to help people develop a scheduling plan of water workers more targeting; therefore, this paper aims to establish a forecast model of daily water consumption based on meteorological conditions. At present, most studies of daily water consumption forecasts focus on historical data or single water use influencing factors; moreover, daily water consumption could be influenced by meteorologic conditions. The influence of complex meteorology factors on water consumption is analyzed based on a gradient-boosted regression tree (GBRT) model. The correlation of 10 meteorologic factors has been discussed and divided into 5 categories, including temperature factor, pressure factor, precipitation factor, sunshine factor, and wind factor. Through the GBRT algorithm, the daily water consumption of residential area could be predicted with a maximum error of ±8%. The results show that the average ground temperature (the feature importance accounts for 81% of the total) has the greatest impact on the daily water consumption of the residential community, followed by the somatosensory temperature (the feature importance accounts for 7% of the total). The method can provide the daily water consumption of water consumption nodes with higher precision for municipal water supply network model accuracy. It also provides a reference for water utility operation schemes and urban development planning.

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Section: Composite Meteorological Factormentioning

confidence: 99%

“…Previous studies have shown that meteorological factors can have an impact on the use of air conditioners during summer and electric heaters in winter [21]. This fluctuation in electrical load can be attributed to changes in weather leading to changes in human comfort.…”

Section: Meteorological Factorsmentioning

confidence: 99%

Prediction of Daily Water Consumption in Residential Areas Based on Meteorologic Conditions—Applying Gradient Boosting Regression Tree Algorithm

Li,

Peng,

Zheng

et al. 2023

Water

View full text Add to dashboard Cite

show abstract

“…Based on the existing literature and the latest situation at home and abroad, we comprehensively consider three influencing factors: meteorological, economic, and significant events. In terms of meteorological factors, we additionally used the apparent temperature [20] as the influencing factor. Compared with the temperature measured by meteorological stations, the apparent temperature(AT) can better reflect the electricity consumption behavior of people due to temperature perception.…”

Section: Multiple Input Featuresmentioning

confidence: 99%

Research On The Daily Electricity Forecast Model Based On LightGBM

Zheng

Chen

et al. 2023

J. Phys.: Conf. Ser.

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Short-term electricity demand forecast is of great significance for guiding the Power Grid Company production operation, but the existing forecast mainly concentrated on load forecast, and most of them only use meteorological and date factors as input features, ignoring the economic factors as a basis to support the growth of electricity, also doesn’t take into account the influence of recent significant events. To improve the accuracy and rationality of short-term electricity demand forecast, based on the Light Gradient Boosting Machine (LightGBM) model, in addition to the common meteorological and date factors, all kinds of economic and significant events are introduced into the model to forecast the daily electricity in this paper. The results show that economic and significant events provide useful information, making our model more accurate than previous short-term electricity demand forecasts. Moreover, due to the adoption of machine learning algorithms, our model is more stable and accurate than traditional measurement models with the same factors.

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“…(8) (9) (10) where indicates the storage unit of long-term memory; indicates the candidate state. When there are multiple layers of recurrent networks, the input of the second layer of recurrent networks is equal to the output of the previous layer output , so the second layer of neural networks carries the historical features extracted from the first layer of networks.…”

Section: Long Short-term Memory Neural Networkmentioning

confidence: 99%

“…The Recurrent Neural Network (RNN) can be well adapted to time series data modeling problems because of its cyclic feedback structure [9]. Long Short-term Memory (LSTM) thoroughly considers the impact of historical data on predictions on the basis of RNN, which can be more effective to reduce the data dimension required by the prediction model, and has higher accuracy than any other machine learning algorithms [10] [12].…”

Section: Introductionmentioning

confidence: 99%

A short-term load forecasting of BiLSTM based on grey relational analysis and attention model

Zeng

Jiang²,

Yang³

et al. 2022

International Workshop on Automation, Control, and Communication Engineering (IWACCE 2022)

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In this paper, a short-term load forecasting of bi-directional long short-term memory (BiLSTM) neural network based on grey relational analysis (GRA) and attention model (AM) is proposed. Firstly, the GRA is used to analyze the correlation between the load and weather factors, and the optimal feature set affecting load is extracted and selected as the input of the prediction model. Then, the AM is used to tune the BiLSTM neural network model parameters. Finally, the BiLSTM neural network model is used for load prediction and being verified with sample data. Compared with other prediction models, the model proposed in this paper shows higher prediction accuracy.

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LSTM load forecasting algorithm based on time-sharing somatosensory

Cited by 3 publications

References 0 publications

Prediction of Daily Water Consumption in Residential Areas Based on Meteorologic Conditions—Applying Gradient Boosting Regression Tree Algorithm

Prediction of Daily Water Consumption in Residential Areas Based on Meteorologic Conditions—Applying Gradient Boosting Regression Tree Algorithm

Research On The Daily Electricity Forecast Model Based On LightGBM

A short-term load forecasting of BiLSTM based on grey relational analysis and attention model

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