Application of Neural Networks for Short Term Load Forecasting in Power System of North Macedonia

Veljanovski, Goran; Atanasovski, Metodija; Kostov, Mitko; Popovski, Pande

doi:10.1109/icest49890.2020.9232674

Cited by 5 publications

(4 citation statements)

References 14 publications

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“…The results show that the random forest gives a high degree of prediction where results for MAPE = 1% and R 2 is greater than 0.99. The study in [17] presents an analysis of electrical load data for 365 days of 2019 in Macedonia using ANN. For model evaluation, the MAE (%), MSE (MW2), and RMSE (MW) error metrics are used.…”

Section: Literature Reviewmentioning

confidence: 99%

Data-Driven Load Forecasting Using Machine Learning and Meteorological Data

Alrashidi¹,

Qamar²

2023

Computer Systems Science and Engineering

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Electrical load forecasting is very crucial for electrical power systems' planning and operation. Both electrical buildings' load demand and meteorological datasets may contain hidden patterns that are required to be investigated and studied to show their potential impact on load forecasting. The meteorological data are analyzed in this study through different data mining techniques aiming to predict the electrical load demand of a factory located in Riyadh, Saudi Arabia. The factory load and meteorological data used in this study are recorded hourly between 2016 and 2017. These data are provided by King Abdullah City for Atomic and Renewable Energy and Saudi Electricity Company at a site located in Riyadh. After applying the data pre-processing techniques to prepare the data, different machine learning algorithms, namely Artificial Neural Network and Support Vector Regression (SVR), are applied and compared to predict the factory load. In addition, for the sake of selecting the optimal set of features, 13 different combinations of features are investigated in this study. The outcomes of this study emphasize selecting the optimal set of features as more features may add complexity to the learning process. Finally, the SVR algorithm with six features provides the most accurate prediction values to predict the factory load.

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Section: Literature Reviewmentioning

confidence: 99%

Data-Driven Load Forecasting Using Machine Learning and Meteorological Data

Alrashidi¹,

Qamar²

2023

Computer Systems Science and Engineering

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“…Neural networks. Neural networks (NN) are used for non-linear time series forecasting in many applications [13,14], [15,16], [17]. These models do not require preliminary assumptions about the form of nonlinearity and are universal approximations [16].…”

Section: Analysis Of Literary Datamentioning

confidence: 99%

Modeling and forecasting of nonlinear nonstationary processes based on the Bayesian structural time series

Kalinina¹,

Gozhyj²

2022

AAIT

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The article describes an approach to modelling and forecasting non-linear non-stationary time series for various purposes using Bayesian structural time series. The concepts of non-linearity and non-stationarity, as well as methods for processing non-linearity’sand non-stationarity in the construction of forecasting models are considered. The features of the Bayesian approach in the processing of nonlinearities and nonstationaryare presented. An approach to the construction of probabilistic-statistical models based on Bayesian structural models of time series has been studied. Parametric and non-parametric methods for forecasting non-linear and non-stationary time series are considered. Parametric methods include methods: classical autoregressive models, neural networks, models of support vector machines, hidden Markov models. Non-parametric methods include methods: state-space models, functional decomposition models, Bayesian non-parametric models. One of the types of non-parametric models isBayesian structural time series. The main features of constructing structural time series are considered. Models of structural time series are presented. The process of learning the Bayesianstructural model of time series is described. Training is performed in four stages: setting the structure of the model and a priori probabilities; applying a Kalman filter to update state estimates based on observed data;application of the “spike-and-slab”method to select variables in a structural model; Bayesian averaging to combine the results to make a prediction. An algorithm for constructing a Bayesian structural time seriesmodel is presented. Various components of the BSTS model are considered andanalysed, with the help of which the structures of alternative predictive models are formed. As an example of the application of Bayesian structural time series, the problem of predicting Amazon stock prices is considered. The base dataset is amzn_share. After loading, the structure and data types were analysed, and missing values were processed. The data are characterized by irregular registration of observations, which leads to a large number of missing values and “masking” possible seasonal fluctuations. This makes the task of forecasting rather difficult. To restore gaps in the amzn_sharetime series, the linear interpolation method was used. Using a set of statistical tests (ADF, KPSS, PP), the series was tested for stationarity. The data set is divided into two parts: training and testing. The fitting of structural models of time series was performed using the Kalman filterand the Monte Carlo method according to the Markov chain scheme. To estimate and simultaneously regularize the regression coefficients, the spike-and-slab method was applied. The quality of predictive models was assessed.

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“…Another example is the work carried out by G. Veljanovski et al [27], in which they proposed a forecasting system based on a neural network, that uses the day type and air temperature of the last seven days as inputs to obtain day-ahead load forecasts as output.…”

Section: Literature Reviewmentioning

confidence: 99%

Reduction of Computational Burden and Accuracy Maximization in Short-Term Load Forecasting

et al. 2022

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Electrical energy is consumed at the same time as it is generated, since its storage is unfeasible. Therefore, short-term load forecasting is needed to manage energy operations. Due to better energy management, precise load forecasting indirectly saves money and CO2 emissions. In Europe, owing to directives and new technologies, prediction systems will be on a quarter-hour basis, which will reduce computation time and increase the computational burden. Therefore, a predictive system may not dispose of sufficient time to compute all future forecasts. Prediction systems perform calculations throughout the day, calculating the same forecasts repeatedly as the predicted time approaches. However, there are forecasts that are no more accurate than others that have already been made. If previous forecasts are used preferentially over these, then computational burden will be saved while accuracy increases. In this way, it will be possible to optimize the schedule of future quarter-hour systems and fulfill the execution time limits. This paper offers an algorithm to estimate which forecasts provide greater accuracy than previous ones, and then make a forecasting schedule. The algorithm has been applied to the forecasting system of the Spanish electricity operator, obtaining a calculation schedule that achieves better accuracy and involves less computational burden. This new algorithm could be applied to other forecasting systems in order to speed up computation times and to reduce forecasting errors.

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Application of Neural Networks for Short Term Load Forecasting in Power System of North Macedonia

Cited by 5 publications

References 14 publications

Data-Driven Load Forecasting Using Machine Learning and Meteorological Data

Data-Driven Load Forecasting Using Machine Learning and Meteorological Data

Modeling and forecasting of nonlinear nonstationary processes based on the Bayesian structural time series

Reduction of Computational Burden and Accuracy Maximization in Short-Term Load Forecasting

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