Empirical Comparison of Neural Network and Auto-Regressive Models in Short-Term Load Forecasting

López, María Dolores Ruiz; Sans, Carlos; Verdú, S.; Senabre, C.

doi:10.3390/en11082080

Cited by 14 publications

(9 citation statements)

References 43 publications

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“…ML algorithms have enhanced the performance of STLF by showing profound accuracy in dealing with non-linearities of the electrical load data and accurate forecasting of the peaks of electrical load as compared to the statistical regression and dimension reduction models [23][24][25]. ML methods mainly comprise Artificial Neural Networks (ANNs) which can handle the stochastic nature of the weather-sensitive loads during the prediction of electrical load forecasting [26][27][28]. The conventional ANN algorithms experience overfitting problems.…”

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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“…Recurrent Neural Networks (RNNs) are ANNs with recurrent weights-that is, neuron outputs from forward layers may be weighed as inputs to neurons from backward layers, creating a sort of internal memory in the network, making this a non-linear dynamic system (which is an advantage), subject to difficulties in the learning process, likely leading to problems than may be without or slower convergence [54] (which are disadvantages).…”

Section: Echo State Networkmentioning

confidence: 99%

“…Also, ESNs are one of the most well-known types of recurrent neural networks because of their excellent performance when nonlinear dynamic system modeling. It is capable to learn the complex nonlinear behaviors of dynamical systems and these have been successfully applied to a wide range of engineering problems, including time series forecasting [49][50][51] and load forecasting [30,[52][53][54][55][56] problems.…”

Section: Introductionmentioning

confidence: 99%

Bayesian Optimized Echo State Network Applied to Short-Term Load Forecasting

et al. 2020

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Load forecasting impacts directly financial returns and information in electrical systems planning. A promising approach to load forecasting is the Echo State Network (ESN), a recurrent neural network for the processing of temporal dependencies. The low computational cost and powerful performance of ESN make it widely used in a range of applications including forecasting tasks and nonlinear modeling. This paper presents a Bayesian optimization algorithm (BOA) of ESN hyperparameters in load forecasting with its main contributions including helping the selection of optimization algorithms for tuning ESN to solve real-world forecasting problems, as well as the evaluation of the performance of Bayesian optimization with different acquisition function settings. For this purpose, the ESN hyperparameters were set as variables to be optimized. Then, the adopted BOA employs a probabilist model using Gaussian process to find the best set of ESN hyperparameters using three different options of acquisition function and a surrogate utility function. Finally, the optimized hyperparameters are used by the ESN for predictions. Two datasets have been used to test the effectiveness of the proposed forecasting ESN model using BOA approaches, one from Poland and another from Brazil. The results of optimization statistics, convergence curves, execution time profile, and the hyperparameters’ best solution frequencies indicate that each problem requires a different setting for the BOA. Simulation results are promising in terms of short-term load forecasting quality and low error predictions may be achieved, given the correct options settings are used. Furthermore, since there is not an optimal global optimization solution known for real-world problems, correlations among certain values of hyperparameters are useful to guide the selection of such a solution.

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“…Compared with physical models, statistical models are designed to observe the changing trend of historical timeseries data to find the mathematical relationship between historical solar irradiance and meteorological parameters. The statistical methods commonly used in solar irradiance prediction include autoregressive (AR) [11], autoregressive moving average, and autoregressive integrated moving average (ARIMA) [12]. Alsharif et al [13] developed a seasonal autoregressive integrated moving average (SARIMA) model to predict the daily and monthly solar radiation in Seoul, South Korea, based on the hourly solar radiation data obtained from the Korean Meteorological Administration over 37 years .…”

Section: Introductionmentioning

confidence: 99%

Prediction of Solar Irradiance One Hour Ahead Based on Quantum Long Short-Term Memory Network

Liu

et al. 2023

IEEE Trans. Quantum Eng.

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The short-term forecasting of photovoltaic (PV) power generation ensures the scheduling and dispatching of electrical power, helps design a PV-integrated energy management system, and enhances the security of grid operation. However, due to the randomness of solar energy, the output of the PV system will fluctuate, which will affect the safe operation of the grid. To solve this problem, a high-precision hybrid prediction model based on variational quantum circuit (VQC) and long short-term memory (LSTM) network is developed to predict solar irradiance 1 hour in advance. VQC is embedded in LSTM to iteratively optimize the weight parameters of four gates (forgetting gate, input gate, cell state, and output gate) to improve prediction accuracy. To evaluate the prediction performance of this model, five solar radiation observatories located in China are selected, together with widely used models including seasonal autoregressive integrated moving average, convolution neural network, recurrent neural network (RNN), gate recurrent unit, (GRU), and LSTM; comparisons are made under different seasons and months. The experimental results show that the annual average root mean square error of the quantum long short-term memory model is 61.756 W/m 2 , which is reduced by 10.7%, 13.9%, 8.1%, 3.8%, and 3.4%, respectively, compared with other models; the annual average mean absolute error is 24.257 W/m 2 , which is reduced by 28.1%, 28.9%, 24.1%, 12.2%, and 12.8%, respectively, compared with other models; the annual average R-Square (R 2 ) is 0.946, which is improved by 1.5%, 1.9%, 1.2%, 0.4%, and 0.4%, respectively, compared with other models. INDEX TERMSLong short-term memory (LSTM) network, quantum neural network, solar irradiance forecasting, variational quantum circuit (VQC). Engineering uantum Transactions on IEEE Yu et al.: PREDICTION OF SOLAR IRRADIANCE ONE HOUR AHEAD BASED ON QUANTUM LSTM NETWORK

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Empirical Comparison of Neural Network and Auto-Regressive Models in Short-Term Load Forecasting

Cited by 14 publications

References 43 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

Bayesian Optimized Echo State Network Applied to Short-Term Load Forecasting

Prediction of Solar Irradiance One Hour Ahead Based on Quantum Long Short-Term Memory Network

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