A Hybrid Approach to Short-Term Load Forecasting Aimed at Bad Data Detection in Secondary Substation Monitoring Equipment

One of the major issues about the operation of power systems is the prediction of load demand. Moreover, load forecasting is of prime concern to system operators. Recently, the integration of power system elements, such as renewable energy sources, energy storages and electricity vehicle, brings more challenges, particularly when there are large fluctuations in forecasting cycle. This study concentrates on short-term load demand forecasting and proposes a hybrid method that combines Singular Spectrum Analysis (SSA) with deep-learning Neural Network (NN) techniques. In the beginning, the SSA technique is applied as an initial filter to remove noises. Next, a hybrid neural network, including Backpropagation Neural Network (BPNN) and Long Short-Term Memory (LSTM), is developed and trained. Then, the trained network is used as the core forecasting algorithm. Each SSA has different forms to combine with neural networks. The performance of the proposed forecasting algorithm is demonstrated using the power demand data recorded in Taiwan. Furthermore, this study compares the forecasting results by five models, including SSA, SSA-BPNN, ANN, SSA-LSTM, Wavelet Neural Network (WNN) and LSTM. The forecasting results reveal that the proposed forecasting model using Singular Spectrum Analysis provides the best performance on load forecasts.

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“…3). This idea is similar to those reported by Y. Chen et al [28]. Daubechies 4 wavelet (Db4) is also applied for load forecasting in this paper.…”

Section: Wavelet Transform Combine Neural Networksupporting

confidence: 79%

A Novel Short-Term Load Forecasting Method by Combining the Deep Learning With Singular Spectrum Analysis

Pham

Nguyen

2021

IEEE Access

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“…The difference between the accuracy of two models should be statistically significant. For this purpose, the forecasting accuracy was validated using statistical tests: Friedman test [48], error analysis [49], Diebold-Mariano (DM) test [50], etc. The performance of the proposed method was validated by two statistical tests, DM and Friedman test.…”

Section: Performance Evaluationmentioning

confidence: 99%

Deep Long Short-Term Memory: A New Price and Load Forecasting Scheme for Big Data in Smart Cities

et al. 2019

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This paper focuses on analytics of an extremely large dataset of smart grid electricity price and load, which is difficult to process with conventional computational models. These data are known as energy big data. The analysis of big data divulges the deeper insights that help experts in the improvement of smart grid’s (SG) operations. Processing and extracting of meaningful information from data is a challenging task. Electricity load and price are the most influential factors in the electricity market. For improving reliability, control and management of electricity market operations, an exact estimate of the day ahead load is a substantial requirement. Energy market trade is based on price. Accurate price forecast enables energy market participants to make effective and most profitable bidding strategies. This paper proposes a deep learning-based model for the forecast of price and demand for big data using Deep Long Short-Term Memory (DLSTM). Due to the adaptive and automatic feature learning mechanism of Deep Neural Network (DNN), the processing of big data is easier with LSTM as compared to the purely data-driven methods. The proposed model was evaluated using well-known real electricity markets’ data. In this study, day and week ahead forecasting experiments were conducted for all months. Forecast performance was assessed using Mean Absolute Error (MAE) and Normalized Root Mean Square Error (NRMSE). The proposed Deep LSTM (DLSTM) method was compared to traditional Artificial Neural Network (ANN) time series forecasting methods, i.e., Nonlinear Autoregressive network with Exogenous variables (NARX) and Extreme Learning Machine (ELM). DLSTM outperformed the compared forecasting methods in terms of accuracy. Experimental results prove the efficiency of the proposed method for electricity price and load forecasting.

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“…A two-day snapshot from the application of this algorithm to raw electrical load data is presented in Figure 5 . The outliers usually originate from noisy sensor readings [ 101 ]. As part of data preprocessing, a resampling step also took place, where each sample was defined as an average of 15 one-minute measurements.…”

Section: Case Studymentioning

confidence: 99%

A Machine Learning Model Ensemble for Mixed Power Load Forecasting across Multiple Time Horizons

Γιαμαρέλος

Papadimitrakis

Stogiannos

et al. 2023

Sensors

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The increasing penetration of renewable energy sources tends to redirect the power systems community’s interest from the traditional power grid model towards the smart grid framework. During this transition, load forecasting for various time horizons constitutes an essential electric utility task in network planning, operation, and management. This paper presents a novel mixed power-load forecasting scheme for multiple prediction horizons ranging from 15 min to 24 h ahead. The proposed approach makes use of a pool of models trained by several machine-learning methods with different characteristics, namely neural networks, linear regression, support vector regression, random forests, and sparse regression. The final prediction values are calculated using an online decision mechanism based on weighting the individual models according to their past performance. The proposed scheme is evaluated on real electrical load data sensed from a high voltage/medium voltage substation and is shown to be highly effective, as it results in R2 coefficient values ranging from 0.99 to 0.79 for prediction horizons ranging from 15 min to 24 h ahead, respectively. The method is compared to several state-of-the-art machine-learning approaches, as well as a different ensemble method, producing highly competitive results in terms of prediction accuracy.

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A Hybrid Approach to Short-Term Load Forecasting Aimed at Bad Data Detection in Secondary Substation Monitoring Equipment

Cited by 12 publications

References 24 publications

A Novel Short-Term Load Forecasting Method by Combining the Deep Learning With Singular Spectrum Analysis

A Novel Short-Term Load Forecasting Method by Combining the Deep Learning With Singular Spectrum Analysis

Deep Long Short-Term Memory: A New Price and Load Forecasting Scheme for Big Data in Smart Cities

A Machine Learning Model Ensemble for Mixed Power Load Forecasting across Multiple Time Horizons

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