Short-term Load Forecasting Using Method of Multiple Linear Regression

Dhaval, Bhatti; Deshpande, Anuradha

doi:10.9734/bpi/naer/v14/13047d

Cited by 4 publications

(4 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…a=2-2t t max (8) where a is the gradually decreased integration factor from 2 to 0, r is indeed random numeric in the range [0,1],and tmax is the highest value reached during the current iteration.…”

Section: The Principle Of the Woa Algorithmmentioning

confidence: 99%

See 1 more Smart Citation

A Short-term Electricity Load Forecasting Method Based on WOA-BiLSTM-Attention

Song

Lan²,

Luo

et al. 2023

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

Under the “double carbon” policy, it is time to build a new green and safe power system. Special attention should be paid to the low accuracy in short-term power load prediction. Therefore, this paper presents a technique for anticipating short-term power demand based on a bidirectional long short-term memory network with a whale-optimized attention mechanism (WOA-BiLSTM-Attention), which is used to forecast and analyze the measured power values in a certain area. The experimental findings reveal that the suggested technique has much greater prediction accuracy and convergence speed, as well as superior stability, when compared to LSTM, BiLSTM, and BiLSTM based on attention mechanism, making it a good reference for power system planning and stability.

show abstract

Section: The Principle Of the Woa Algorithmmentioning

confidence: 99%

“…Common prediction techniques can be classified into three categories. The first group includes conventional prediction techniques:the exponential smoothing method [7], the multiple linear regression (MLR) [8], the time series method [9], the Kalman filtering method [10], and the grey prediction method [11]. They have apparent advantages.…”

Section: Introductionmentioning

confidence: 99%

A Short-term Electricity Load Forecasting Method Based on WOA-BiLSTM-Attention

Song

Lan²,

Luo

et al. 2023

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

show abstract

“…Therefore, developing a prediction algorithm to improve the accuracy of prediction is an urgent problem that needs to be solved. Some single-variable time series models including Autoregressive Integrated Moving Average (ARIMA) [3], linear regression [4], and Exponentiated Linear Regression (ELR) [5] are widely used for predicting stationary time series. A predictive model [6] based on ARIMA [3] was proposed for energy consumption prediction.…”

Section: Introductionmentioning

confidence: 99%

Load Prediction in Double-Channel Residual Self-Attention Temporal Convolutional Network with Weight Adaptive Updating in Cloud Computing

Lin,

Guan

2024

Sensors

View full text Add to dashboard Cite

When resource demand increases and decreases rapidly, container clusters in the cloud environment need to respond to the number of containers in a timely manner to ensure service quality. Resource load prediction is a prominent challenge issue with the widespread adoption of cloud computing. A novel cloud computing load prediction method has been proposed, the Double-channel residual Self-attention Temporal convolutional Network with Weight adaptive updating (DSTNW), in order to make the response of the container cluster more rapid and accurate. A Double-channel Temporal Convolution Network model (DTN) has been developed to capture long-term sequence dependencies and enhance feature extraction capabilities when the model handles long load sequences. Double-channel dilated causal convolution has been adopted to replace the single-channel dilated causal convolution in the DTN. A residual temporal self-attention mechanism (SM) has been proposed to improve the performance of the network and focus on features with significant contributions from the DTN. DTN and SM jointly constitute a dual-channel residual self-attention temporal convolutional network (DSTN). In addition, by evaluating the accuracy aspects of single and stacked DSTNs, an adaptive weight strategy has been proposed to assign corresponding weights for the single and stacked DSTNs, respectively. The experimental results highlight that the developed method has outstanding prediction performance for cloud computing in comparison with some state-of-the-art methods. The proposed method achieved an average improvement of 24.16% and 30.48% on the Container dataset and Google dataset, respectively.

show abstract

“…To address these challenges, is crucial for developing efficient and accurate time series prediction algorithms to achieve high availability and performance in cloud computing environments. Some single-variable time series models including Autoregressive Integrated Moving Average (ARIMA) [3], Linear regression [4], and Exponentiated Linear Regression (ELR) [5] are widely used for predicting stationary time series. A predictive model [6] based on ARIMA [3] was proposed for energy consumption prediction.…”

Section: Introductionmentioning

confidence: 99%

Cloud Computing Load Prediction in Double-channel Residual Self-attention Temporal Convolutional Network with Weight Adaptive Updating

Jiang

Guan

2023

Preprint

View full text Add to dashboard Cite

Resource load prediction is a prominent challenge issue with the widespread adoption of cloud computing. A novel cloud computing load prediction method has been proposed in Double-channel residual Self-attention Temporal convolutional Network with Weight adaptive updating (DSTNW). A Double-channel Temporal convolution Network model (DTN) has been developed. The double-channel dilated causal convolution has been adopted to replace a single channel dilated causal convolution in the DTN. A residual temporal self-attention mechanism has been proposed to improve the performance of network and focus on features with significant contributions from the DTN. Some errors for single and stacked Double-channel residual Self-attention Temporal convolutional Network (DSTN) have been evaluated. An adaptive weight strategy has been proposed to assign corresponding weights for the single and stacked DSTNs, respectively. Experimental results highlight that the developed method has outstanding prediction performance for cloud computing in comparison with some state-of-the-arts.

show abstract

Short-term Load Forecasting Using Method of Multiple Linear Regression

Cited by 4 publications

References 0 publications

A Short-term Electricity Load Forecasting Method Based on WOA-BiLSTM-Attention

A Short-term Electricity Load Forecasting Method Based on WOA-BiLSTM-Attention

Load Prediction in Double-Channel Residual Self-Attention Temporal Convolutional Network with Weight Adaptive Updating in Cloud Computing

Cloud Computing Load Prediction in Double-channel Residual Self-attention Temporal Convolutional Network with Weight Adaptive Updating

Contact Info

Product

Resources

About