Projection Design for Compressive Source Separation Using Mean Errors and Cross-Validation

Shah, Dhruv; Rajwade, Ajit

doi:10.1109/icip.2019.8803202

Cited by 1 publication

(3 citation statements)

References 33 publications

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“…In addition, a Mean Absolute Error (MAE) to measure inaccuracy in the data was used. The difference between actual values and accurate values is known as the absolute error, and the average of these absolute errors is known as the mean absolute error [66,67]. It can be calculated using the formula shown in Equation (7).…”

Section: Model Evaluationmentioning

confidence: 99%

“…Moreover, the Mean Squared Error (MSE) was used to measure the difference between prediction and the actual value, the average of the squared absolute errors. It is calculated by first squaring the absolute error and then taking their average [66]. The formula is shown in Equation (8).…”

Section: Model Evaluationmentioning

confidence: 99%

“…The formula is shown in Equation (8). Additionally, Root Mean Squared Error (RMSE) is used to measure the error between the actual data and the model of estimation [54][55][56][57][58][59][60][61][62][63][64][65][66]. It is calculated by simply taking the root of MSE.…”

Section: Model Evaluationmentioning

confidence: 99%

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Smart Grid Data Management in a Heterogeneous Environment with a Hybrid Load Forecasting Model

et al. 2021

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The power consumption model can be represented in multiple dimensions, and it is proliferating to include structured and unstructured data. Dealing with such heterogeneous data and analyzing it in real-time is an ongoing challenge in the energy sector. Moreover, converting these data into useful information remains an open research area. This study focuses on modeling realistic and efficient power consumption data management in the heterogeneous environment for the Iraq energy sector and suggested a novel hybrid load forecasting model. The proposed system is named the Power Consumption Information and Analytics System (PIAS), which can perform various roles such as data acquisition from mechanical and smart meters, data federation, data management, data visualization, data analysis, and load forecasting. The proposed system has a four-tier framework (Data, Analytics, Application, and Presentation). Each layer is discussed in detail in this study to overcome the anticipated challenges. Furthermore, this study discusses the proposed system by applying two case studies. The first case study discusses power consumption data management, while the second introduces a novel hybrid load forecasting model using Fuzzy C-Means clustering, Auto Regressive Integrated Moving Average (ARIMA), and Gradient Boosted Tree Learner. The dataset used in this forecasting is based on a 1-year duration dated 1st January 2019 to 31st December 2019, on an hourly basis (365 * 24) for the Baghdad governorate. The results showed high accuracy in load forecasting with improved error rates (MAPE, MAE, and RMSE) achievements in comparison with other evaluated models such as standalone ARIMA and Gradient Boosted Trees methods.

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Section: Model Evaluationmentioning

confidence: 99%

Section: Model Evaluationmentioning

confidence: 99%