Intelligent Systems for Power Load Forecasting: A Study Review

Jahan, Ibrahim Salem; Snåšel, Václav; Mišák, Stanislav

doi:10.3390/en13226105

Cited by 51 publications

(25 citation statements)

References 49 publications

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“…Moreover, the segmentation of electrical load data into IMFs using EMD and the neural network training of the captured IMFs separately using DBNs require a high computational time [41]. Additionally, an Adaptive Network-based Fuzzy Inference System (ANFIS) enlarges the learning capacity of NNs by fusing neural architecture with high-level reasoning methodologies of fuzzy logic [42]. However, the intricate rules, a large number of antecedents and mode delays increase the complexity of the learning phase in ANFIS architectures [43].…”

Section: Literature Reviewmentioning

confidence: 99%

Exploratory Data Analysis Based Short-Term Electrical Load Forecasting: A Comprehensive Analysis

et al. 2021

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Power system planning in numerous electric utilities merely relies on the conventional statistical methodologies, such as ARIMA for short-term electrical load forecasting, which is incapable of determining the non-linearities induced by the non-linear seasonal data, which affect the electrical load. This research work presents a comprehensive overview of modern linear and non-linear parametric modeling techniques for short-term electrical load forecasting to ensure stable and reliable power system operations by mitigating non-linearities in electrical load data. Based on the findings of exploratory data analysis, the temporal and climatic factors are identified as the potential input features in these modeling techniques. The real-time electrical load and meteorological data of the city of Lahore in Pakistan are considered to analyze the reliability of different state-of-the-art linear and non-linear parametric methodologies. Based on performance indices, such as Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE) and Mean Absolute Error (MAE), the qualitative and quantitative comparisons have been conferred among these scientific rationales. The experimental results reveal that the ANN–LM with a single hidden layer performs relatively better in terms of performance indices compared to OE, ARX, ARMAX, SVM, ANN–PSO, KNN, ANN–LM with two hidden layers and bootstrap aggregation models.

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Section: Literature Reviewmentioning

confidence: 99%

Exploratory Data Analysis Based Short-Term Electrical Load Forecasting: A Comprehensive Analysis

et al. 2021

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“…In Ref. [ 20 ], different load forecasting techniques were reviewed and compared for power forecasting. These methods include ANN, Support Vector Regression (SVR), Decision Tree (DT), LR, and Fuzzy Sets (FS).…”

Section: Literature Reviewmentioning

confidence: 99%

An adaptive backpropagation algorithm for long-term electricity load forecasting

Mohammed¹,

Al-Bazi

2021

Neural Comput & Applic

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Artificial Neural Networks (ANNs) have been widely used to determine future demand for power in the short, medium, and long terms. However, research has identified that ANNs could cause inaccurate predictions of load when used for long-term forecasting. This inaccuracy is attributed to insufficient training data and increased accumulated errors, especially in long-term estimations. This study develops an improved ANN model with an Adaptive Backpropagation Algorithm (ABPA) for best practice in the forecasting long-term load demand of electricity. The ABPA includes proposing new forecasting formulations that adjust/adapt forecast values, so it takes into consideration the deviation between trained and future input datasets' different behaviours. The architecture of the Multi-Layer Perceptron (MLP) model, along with its traditional Backpropagation Algorithm (BPA), is used as a baseline for the proposed development. The forecasting formula is further improved by introducing adjustment factors to smooth out behavioural differences between the trained and new/future datasets. A computational study based on actual monthly electricity consumption inputs from 2011 to 2020, provided by the Iraqi Ministry of Electricity, is conducted to verify the proposed adaptive algorithm's performance. Different types of energy consumption and the electricity cut period (unsatisfied demand) factor are also considered in this study as vital factors. The developed ANN model, including its proposed ABPA, is then compared with traditional and popular prediction techniques such as regression and other advanced machine learning approaches, including Recurrent Neural Networks (RNNs), to justify its superiority amongst them. The results reveal that the most accurate long-term forecasts with the minimum Mean Squared Error (MSE) and Mean Absolute Percentage Error (MAPE) values of (1.195.650) and (0.045), respectively, are successfully achieved by applying the proposed ABPA. It can be concluded that the proposed ABPA, including the adjustment factor, enables traditional ANN techniques to be efficiently used for long-term forecasting of electricity load demand.

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“…Many different approaches to load forecasting were tried in the past. A good review of such methods is presented in [1]. The developed approaches start from linear ARMA, SARIMA, and ARMAX [2,3] and end on much more advanced nonlinear models based on neural networks [4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

High Precision LSTM Model for Short-Time Load Forecasting in Power Systems

Ciechulski

Osowski

2021

Energies

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The paper presents the application of recurrent LSTM neural networks for short-time load forecasting in the Polish Power System (PPS) and a small region of a power system in Central Poland. The objective of the present work was to develop an efficient and accurate method of forecasting the 24-h pattern of power load with a 1-h and 24-h horizon. LSTM showed effectiveness in predicting the irregular trends in time series. The final forecast is estimated using an ensemble consisted of five independent predictions. Numerical experiments proved the superiority of the ensemble above single predictor resulting in a reduction of the MAPE the RMSE error by more than 6% in both forecasting tasks.

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Intelligent Systems for Power Load Forecasting: A Study Review

Cited by 51 publications

References 49 publications

Exploratory Data Analysis Based Short-Term Electrical Load Forecasting: A Comprehensive Analysis

Exploratory Data Analysis Based Short-Term Electrical Load Forecasting: A Comprehensive Analysis

An adaptive backpropagation algorithm for long-term electricity load forecasting

High Precision LSTM Model for Short-Time Load Forecasting in Power Systems

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