A Comprehensive Review of the Load Forecasting Techniques Using Single and Hybrid Predictive Models

Mamun, Abdullahil; Sohel, Md.; Mohammad, Naeem; Sunny, Md Samiul Haque; Dipta, Debopriya Roy; Hossain, Eklas

doi:10.1109/access.2020.3010702

Cited by 235 publications

(108 citation statements)

References 152 publications

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“…PLS is a straightforward dimensionality reduction technique that maps the variables in a new feature space with lower dimensions. The Variable Importance of load Patterns (VIP) for 32 features is shown in Regarding Figure 9, the most important features are hour, workday, temperature and lagged load (t − x) with x ∈ [1,2,3,4,5,6,7,11,12,13,17,18,19,20,21,22,23]. Thus, the selected threshold is VIP = 0.5.…”

Section: Data Pre-processing and Feature Engineeringmentioning

confidence: 99%

“…In order to standardize the SDEM measures, particularly, the Rooted Mean Squared Error (RMSE) and Mean Absolute Error (MAE) measures, the Min-Max normalization method was kept for the evaluation process. In other words, the data is normalized with a magnitude range of [0,1]. The data normalization is calculated as follows: (23) where x t denote the normalized value, x r is the real value.…”

Section: Score Metricsmentioning

confidence: 99%

“…For power systems, grid stability is becoming very sensible to the bulk integration of renewable energy sources due to the irregularity and seasonality of numerous external factors. These factors fall into two major aspects: (i) Meteorological, such as temperature, irradiation, and wind speed, and (ii) social, such as working days, lifestyles, and number of consumers [1]. Thus, grid sustainability is threatened by the high fluctuations in the load profiles.…”

Section: Introductionmentioning

confidence: 99%

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PLS-CNN-BiLSTM: An End-to-End Algorithm-Based Savitzky–Golay Smoothing and Evolution Strategy for Load Forecasting

et al. 2020

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This paper proposes an effective deep learning framework for Short-Term Load Forecasting (STLF) of multivariate time series. The proposed model consists of a hybrid Convolutional neural network-Bidirectional Long Short-Term Memory (CBiLSTM) based on the Evolution Strategy (ES) method and the Savitzky–Golay (SG) filter (SG-CBiLSTM). The adopted methodology incorporates the virtue of different prepossessing blocks to enhance the performance of the CBiLSTM model. In particular, a data-augmentation strategy is employed to synthetically improve the feature representation of the CBiLSTM model. The augmented data is forwarded to the Partial Least Square (PLS) method to select the most informative features above the predefined threshold. Next, the SG algorithm is computed for smoothing the load to enhance the learning capabilities of the underlying system. The structure of the SG-CBiLSTM for the ISO New England dataset is optimized using the ES technique. Finally, the CBiLSTM model generates output forecasts. The proposed approach demonstrates a remarkable improvement in the performance of the original CBiLSTM model. Furthermore, the experimental results strongly confirm the high effectiveness of the proposed SG-CBiLSTM model compared to the state-of-the-art techniques.

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Section: Data Pre-processing and Feature Engineeringmentioning

confidence: 99%

Section: Score Metricsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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PLS-CNN-BiLSTM: An End-to-End Algorithm-Based Savitzky–Golay Smoothing and Evolution Strategy for Load Forecasting

et al. 2020

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“…Electrical load forecasting is a traditional problem, which has been solved with a number of methods, from statistical methods to approaches that are based on artificial intelligence, in particular neural networks and support vector machines, or more recently based on deep learning [68,69]. Today, the increase of the generation from renewable energy sources has introduced the further high uncertainty, depending on solar irradiance, wind speed and direction, and energy prices.…”

Section: Load and Generation Forecasting (Lgf)mentioning

confidence: 99%

Metaheuristic Optimization of Power and Energy Systems: Underlying Principles and Main Issues of the ‘Rush to Heuristics’

Chicco

Mazza

2020

Energies

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In the power and energy systems area, a progressive increase of literature contributions that contain applications of metaheuristic algorithms is occurring. In many cases, these applications are merely aimed at proposing the testing of an existing metaheuristic algorithm on a specific problem, claiming that the proposed method is better than other methods that are based on weak comparisons. This ‘rush to heuristics’ does not happen in the evolutionary computation domain, where the rules for setting up rigorous comparisons are stricter but are typical of the domains of application of the metaheuristics. This paper considers the applications to power and energy systems and aims at providing a comprehensive view of the main issues that concern the use of metaheuristics for global optimization problems. A set of underlying principles that characterize the metaheuristic algorithms is presented. The customization of metaheuristic algorithms to fit the constraints of specific problems is discussed. Some weaknesses and pitfalls that are found in literature contributions are identified, and specific guidelines are provided regarding how to prepare sound contributions on the application of metaheuristic algorithms to specific problems.

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“…Because the relationship between many parameters is complex and unstable, electric forecasting can be separated depending on weather conditions and forward load structure. Various machine learning techniques have been proposed using algorithms of varying qualities to predict power loads [4]. Due to the rapid economic growth driven by population growth and the steady increase in electricity demand in large cities, power forecasting plays an essential role in monthly time measurements.…”

Section: Introductionmentioning

confidence: 99%

Genetic Algorithm Based Optimized Feature Engineering and Hybrid Machine Learning for Effective Energy Consumption Prediction

Byun

2020

IEEE Access

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Smart grids are developing rapidly, leading to the need for accurate forecasts of power consumption. However, developing a precise time series model for energy forecasting is difficult. It has to be trained using optimal meteorological features such as temperature and time lags to qualify for a beneficial model. We have proposed an approach that uses an ensemble machine learning model based on XGBoost, support vector regressor (SVR), and K-nearest neighbors (KNN) regressor algorithms. We have also used the genetic algorithm (GA) to predict total load consumption from optimal feature selection. Using Jeju island's electricity consumption data as a case study shows that the proposed ensemble model optimized with GA is more accurate than the individual machine learning models. Using only the best-selected weather and time features, the proposed model records all the features of a complicated time series and shows a reduction in the mean absolute percentage error (MAPE) and the root mean square log error for the week ahead forecasts. We got 3.35 % MAPE of the three months test data by applying the proposed model. The smart grids operators can manage resources effectively to provide excellent services to the consumers based on the recommended model outcomes.

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A Comprehensive Review of the Load Forecasting Techniques Using Single and Hybrid Predictive Models

Cited by 235 publications

References 152 publications

PLS-CNN-BiLSTM: An End-to-End Algorithm-Based Savitzky–Golay Smoothing and Evolution Strategy for Load Forecasting

PLS-CNN-BiLSTM: An End-to-End Algorithm-Based Savitzky–Golay Smoothing and Evolution Strategy for Load Forecasting

Metaheuristic Optimization of Power and Energy Systems: Underlying Principles and Main Issues of the ‘Rush to Heuristics’

Genetic Algorithm Based Optimized Feature Engineering and Hybrid Machine Learning for Effective Energy Consumption Prediction

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