A new interval prediction methodology for short-term electric load forecasting based on pattern recognition

Serrano-Guerrero, Xavier; Briceño-León, Marco; Clairand, Jean-Michel; Escrivá-Escrivá, Guillermo

doi:10.1016/j.apenergy.2021.117173

Cited by 34 publications

(10 citation statements)

References 33 publications

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“…The correlation degree is indicated by each color block in the figure，the deeper the color block, the higher the correlation. The correlation degree, which can be employed as a load influencing factor, is larger than or equivalent to 0.3 [21]. The strongest correlation, 0.589, between surface temperature and load may be found in Fig.…”

Section: Spatial-temporal Fusion Of Multi-dimensional Meteorological ...mentioning

confidence: 90%

Short Term Electrical Load Combination Forecasting Model based on Multi-dimensional Meteorological Information Spatio-Temporal Fusion and MPA-VMD

Wang

Zhou

et al. 2023

Preprint

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This paper considers the variability in the impact of multi-dimensional meteorological information on power load in different regions. To improve the accuracy of load forecasting in the spatial dimension, the method of spatio-temporal fusion (SF) of multi-dimensional meteorological information is proposed. The Copula theory is applied to analyze the nonlinear coupling of meteorological information such as wind speed, rainfall, temperature, and sunshine intensity from multiple meteorological stations with the power load and to achieve spatio-temporal fusion. In the time dimension, the core parameters of the variational mode decomposition (VMD) are improved by the marine predators algorithm (MPA). The weighted permutation entropy (WPE) is used to construct the MPA-VMD fitness function for the adaptive decomposition of the load sequence. In addition, the input sets of the LSTM model and MPA-LSSVM model are constructed by combining each component of the time dimension and each meteorological information of spatial dimension to obtain the prediction results of each component. The prediction model corresponding to each component is selected according to the evaluation index, and reconstructed to obtain the overall prediction results. The analysis results show that the proposed forecasting method is better than the traditional forecasting method and effectively improves the accuracy of power load forecasting.

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Section: Spatial-temporal Fusion Of Multi-dimensional Meteorological ...mentioning

confidence: 90%

Short Term Electrical Load Combination Forecasting Model based on Multi-dimensional Meteorological Information Spatio-Temporal Fusion and MPA-VMD

Wang

Zhou

et al. 2023

Preprint

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show abstract

“…The correlation degree is indicated by each colour block in the figure; the deeper the colour block, the higher the correlation. The correlation degree, which can be employed as a load influencing factor, is larger than or equivalent to 0.3 [21].…”

Section: Related Algorithm Basismentioning

confidence: 99%

Short‐term electrical load forecasting model based on multi‐dimensional meteorological information spatio‐temporal fusion and optimized variational mode decomposition

Wang,

Zhou,

et al. 2023

IET Generation Trans & Dist

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This paper proposes a method to enhance the accuracy of power load forecasting by considering the variability in the impact of multi‐dimensional meteorological information on power load in diverse regions. The proposed method employs spatio‐temporal fusion (SF) of multi‐dimensional meteorological information and applies the Copula theory to analyze the non‐linear coupling of meteorological information from multiple stations with power load to achieve SF in the spatial dimension. To enhance the accuracy of load forecasting in the time dimension, this paper improves the core parameters of the variational mode decomposition (VMD) using the marine predators algorithm (MPA) and utilizes the weighted permutation entropy (WPE) to construct the MPA‐VMD fitness function for the adaptive decomposition of the load sequence. Moreover, this paper constructs input sets for the long short‐term memory model and the MPA‐LSSVM model by combining each component of the time dimension and each meteorological information of the spatial dimension to obtain the prediction results of each component. The prediction model corresponding to each component is selected according to the evaluation index and reconstructed to obtain the overall prediction results. The analysis results demonstrate that the proposed forecasting method outperforms the traditional forecasting method and effectively enhances the accuracy of power load forecasting.

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“…Univariate time series forecasting takes an input dataset of a single field of values at evenly spaced intervals in time and uses previous patterns in the dataset to make predictions about the future values and trend [3,7]. Multivariate forecasting [8] may use contextual information or other data fields to assist in the prediction of future values.…”

Section: B Time Series Forecastingmentioning

confidence: 99%

“…Both methods of forecasting can require fitting to prior data before forecasting. While large volumes of data are not necessarily required to predict a future event, having enough data to observe previous patterns in the data is often required for an accurate forecast [7]. Accuracy may increase with volume of data based on stationarity or variance of data, relevance, and overall concept of the data [6,9].…”

Section: B Time Series Forecastingmentioning

confidence: 99%

“…Accuracy may increase with volume of data based on stationarity or variance of data, relevance, and overall concept of the data [6,9]. Uncertainty in the forecast can be mitigated using forecast intervals [7], allowing for a forecast range to be produced rather than an absolute value. Many time-series predictions require that the model be re-fitted in order to include a new data point into the prediction; this introduces computational complexity into any system requiring live or regular predictions.…”

Section: B Time Series Forecastingmentioning

confidence: 99%

See 1 more Smart Citation

Day ahead Power Demand Forecasting for Hybrid Power at the Edge

McCormack

Wallace²,

Barrie

et al. 2022

2022 IEEE World AI IoT Congress (AIIoT)

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We describe the investigation and testing of univariate forecasting techniques on IoT hardware for application at "the Edge" using power demand forecasting. An evaluation of common forecasting techniques is presented, tested using the Morocco Buildings Electricity Consumption Datasets. An architecture is described for the Edge system that would enable 1-day forward forecasts of power demand for use in provisioning power in a hybrid power system. Several of the configurations examined in this study performed comparably with current trends in forecasting methods and are suitable for this application at the Edge, providing a balance of performance and accuracy. A Long Short-Term Memory (LSTM) Neural Network configuration provided the most effective balance of performance, accuracy and simplicity of deployment that is desirable for an application at the Edge.

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A new interval prediction methodology for short-term electric load forecasting based on pattern recognition

Cited by 34 publications

References 33 publications

Short Term Electrical Load Combination Forecasting Model based on Multi-dimensional Meteorological Information Spatio-Temporal Fusion and MPA-VMD

Short Term Electrical Load Combination Forecasting Model based on Multi-dimensional Meteorological Information Spatio-Temporal Fusion and MPA-VMD

Short‐term electrical load forecasting model based on multi‐dimensional meteorological information spatio‐temporal fusion and optimized variational mode decomposition

Day ahead Power Demand Forecasting for Hybrid Power at the Edge

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