Short-term load forecasting with using multiple linear regression

Dhaval, Bhatti; Deshpande, Anuradha

doi:10.11591/ijece.v10i4.pp3911-3917

Cited by 27 publications

(23 citation statements)

References 12 publications

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“…As shown in Table 2, the three load seasons have significant load autocorrelation because the Pvalue is smaller than the significance level. In our analyses, the statistical confidence interval is 95% and the significance level is 5%, similar to [10]. Table 2 shows the main statistical output of the NBAM when Aqabat Jaber is the response variable.…”

Section: Training Of the Proposed Temporal Nbammentioning

confidence: 80%

“…A single season for the HW, the ARMA and the ARIMA models is used because they are cyclic-based models and need a repeated pattern. The Mean Absolute Percentage Error (MAPE) is calculated to compare the accuracy of the models forecasting results, similar to [3,10]. Table 3 shows the MAPE values of the five models for the selected stations during the three load seasons for a ten-hours horizon.…”

Section: Forecasting Accuracymentioning

confidence: 99%

“…The high fluctuation in electric load causes overdispersion expressed by a large variance value that is clearly larger than the mean [9]. Research states that electric load fluctuation depends on the life style, day time and location [10,11]. Overdispersion reduces the accuracy if it is not handled properly, particularly in the short-term forecasting, because forecasting methods are sensitive to the fluctuation [12].…”

Section: Introductionmentioning

confidence: 99%

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Forecasting for smart energy: an accurate and effificient negative binomial additive model

Daraghmi

Daadoo

et al. 2020

IJEECS

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<div>Smart energy requires accurate and effificient short-term electric load forecasting to enable effificient</div><div>energy management and active real-time power control. Forecasting accuracy is inflfluenced by the char</div><div>acteristics of electrical load particularly overdispersion, nonlinearity, autocorrelation and seasonal patterns.</div><div>Although several fundamental forecasting methods have been proposed, accurate and effificient forecasting</div><div>methods that can consider all electric load characteristics are still needed. Therefore, we propose a novel</div><div>model for short-term electric load forecasting. The model adopts the negative binomial additive models</div><div>(NBAM) for handling overdispersion and capturing the nonlinearity of electric load. To address the season</div><div>ality, the daily load pattern is classifified into high, moderate, and low seasons, and the autocorrelation of</div><div>load is modeled separately in each season. We also consider the effificiency of forecasting since the NBAM</div><div>captures the behavior of predictors by smooth functions that are estimated via a scoring algorithm which has</div><div>low computational demand. The proposed NBAM is applied to real-world data set from Jericho city, and its</div><div>accuracy and effificiency outperform those of the other models used in this context.</div>

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Section: Training Of the Proposed Temporal Nbammentioning

confidence: 80%

Section: Forecasting Accuracymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Forecasting for smart energy: an accurate and effificient negative binomial additive model

Daraghmi

Daadoo

et al. 2020

IJEECS

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“…Probably the most common and simple modelling method is linear regression [53]. Thus, a least squares regression model was built and used as baseline; it should be noted that in this case, there are no hyper-parameters to be optimized.…”

Section: Experimental Protocol -Implementation Detailsmentioning

confidence: 99%

Short-Term Electric Load Forecasting With Sparse Coding Methods

et al. 2021

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Short-term load forecasting is a key task for planning and stability of the current and future distribution grid, as it can significantly contribute to the management of energy market for ancillary services. In this paper we introduce the beneficial properties of applications of sparse representation and corresponding dictionary learning to the net load forecasting problem on a substation level. In this context, sparse representation theory can provide parsimonial predictive models, which become attractive mainly due to their ability to successfully model the input space in a self-learning manner, by interacting between theory, algorithms, and applications. Several techniques are implemented, incorporating numerous dictionary learning and sparse decomposition algorithms, and a hierarchical structured model is proposed. The concept of sparsity in each case is embedded throughout the utilization of different regularization forms which include the 0 , 1 , 2 and tree 0 norms. The observed superiority of the proposed theory, especially the one which embeds the atoms and corresponding coefficients in a tree structure, stems from the construction of the dictionary so as to represent efficiently the ambient electricity signal space and the consequent extraction of sparse basis-vectors. The performance of each model is evaluated using real hourly load measurements from a high voltage/medium voltage (HV/MV) substation and compared with that of widely used machine learning methods. The provided analytical results, verify the effectiveness of hierarchical sparse representation in short-term load forecasting applications, in terms of common accuracy indices.INDEX TERMS Generative models, hierarchical dictionaries, load forecasting, power grid, sparse representation.

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“…In addition, compared to machine learning, deep learning requires stronger processors and larger training data for its results. Compared to ANN, deep learning offers more layers working [31][32][33][34][35][36][37].…”

Section: Introductionmentioning

confidence: 99%

Short-term wind speed forecasting system using deep learning for wind turbine applications

Erdemir

Zengin

Akıncı

2020

IJECE

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It is very important to accurately detect wind direction and speed for wind energy that is one of the essential sustainable energy sources. Studies on the wind speed forecasting are generally carried out for long-term predictions. One of the main reasons for the long-term forecasts is the correct planning of the area where the wind turbine will be built due to the high investment costs and long-term returns. Besides that, short-term forecasting is another important point for the efficient use of wind turbines. In addition to estimating only average values, making instant and dynamic short-term forecasts are necessary to control wind turbines. In this study, short-term forecasting of the changes in wind speed between 1-20 minutes using deep learning was performed. Wind speed data was obtained instantaneously from the feedback of the emulated wind turbine's generator. These dynamically changing data was used as an input of the deep learning algorithm. Each new data from the generator was used as both test and training input in the proposed approach. In this way, the model accuracy and enhancement were provided simultaneously. The proposed approach was turned into a modular independent integrated system to work in various wind turbine applications. It was observed that the system can predict wind speed dynamically with around 3% error in the applications in the test setup applications.

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Short-term load forecasting with using multiple linear regression

Cited by 27 publications

References 12 publications

Forecasting for smart energy: an accurate and effificient negative binomial additive model

Forecasting for smart energy: an accurate and effificient negative binomial additive model

Short-Term Electric Load Forecasting With Sparse Coding Methods

Short-term wind speed forecasting system using deep learning for wind turbine applications

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