Multilayer perceptron for short-term load forecasting: from global to local approach

Dudek, Grzegorz

doi:10.1007/s00521-019-04130-y

Cited by 55 publications

(27 citation statements)

References 37 publications

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“…This is a class of feedforward artificial neural networks (ANNs). MLP is a popular and effective non-linear or linear (depending on the type of activation function in hidden layer/layers and output layer) global approximator [51,52]. It consists of one input layer, typically has one or two hidden layers, one output layer and often uses the backpropagation algorithm for supervised learning.…”

Section: Forecasting Methodsmentioning

confidence: 99%

Forecasting of 10-Second Power Demand of Highly Variable Loads for Microgrid Operation Control

et al. 2021

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This paper addresses very short-term (10 s) forecasting of power demand of highly variable loads. The main purpose of this study is to develop methods useful for this type of forecast. We have completed a comprehensive study using two different time series, which are very difficult to access in practice, of 10 s power demand characterized by big dynamics of load changes. This is an emerging and promising forecasting research topic, yet to be more widely recognized in the forecasting research community. This problem is particularly important in microgrids, i.e., small energy micro-systems. Power demand forecasting, like forecasting of renewable power generation, is of key importance, especially in island mode operation of microgrids. This is due to the necessity of ensuring reliable power supplies to consumers. Inaccurate very short-term forecasts can cause improper operation of microgrids or increase costs/decrease profits in the electricity market. This paper presents a detailed statistical analysis of data for two sample low voltage loads characterized by large variability, which are located in a sewage treatment plant. The experience of the authors of this paper is that very short-term forecasting is very difficult for such loads. Special attention has been paid to different forecasting methods, which can be applied to this type of forecast, and to the selection of explanatory variables in these methods. Some of the ensemble models (eight selected models belonging to the following classes of methods: random forest regression, gradient boosted trees, weighted averaging ensemble, machine learning) proposed in the scope of choice of methods sets constituting the models set are unique models developed by the authors of this study. The obtained forecasts are presented and analyzed in detail. Moreover, qualitative analysis of the forecasts obtained has been carried out. We analyze various measures of forecasts quality. We think that some of the presented forecasting methods are promising for practical applications, i.e., for microgrid operation control, because of their accuracy and stability. The analysis of usefulness of various forecasting methods for two independent time series is an essential, very valuable element of the study carried out. Thanks to this, reliability of conclusions concerning the preferred methods has considerably increased.

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Section: Forecasting Methodsmentioning

confidence: 99%

Forecasting of 10-Second Power Demand of Highly Variable Loads for Microgrid Operation Control

et al. 2021

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“…It is a class of feedforward ANNs. MLP is a popular and effective non-linear or linear (depending on the type of activation function in hidden layer(s) and output layer) global approximator [29,36,39]. It consists of a single input layer, typically has one or two hidden layers, one output layer and uses the backpropagation algorithm for supervised learning.…”

Section: Setmentioning

confidence: 99%

Hybrid and Ensemble Methods of Two Days Ahead Forecasts of Electric Energy Production in a Small Wind Turbine

et al. 2021

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The ability to forecast electricity generation for a small wind turbine is important both on a larger scale where there are many such turbines (because it creates problems for networks managed by distribution system operators) and for prosumers to allow current energy consumption planning. It is also important for owners of small energy systems in order to optimize the use of various energy sources and facilitate energy storage. The research presented here addresses an original, rarely predicted 48 h forecasting horizon for small wind turbines. This topic has been rather underrepresented in research, especially in comparison with forecasts for large wind farms. Wind speed forecasts with a 48 h horizon are also rarely used as input data. We have analyzed the available data to identify potentially useful explanatory variables for forecasting models. Eight sets with increasing data amounts were created to analyze the influence of the types and amounts of data on forecast quality. Hybrid, ensemble and single methods are used for predictions, including machine learning (ML) solutions like long short-term memory (LSTM), multi-layer perceptron (MLP), support vector regression (SVR) and K-nearest neighbours regression (KNNR). Original hybrid methods, developed for research of specific implementations and ensemble methods based on hybrid methods’ decreased errors of energy generation forecasts for small wind turbines in comparison with single methods. The “artificial neural network (ANN) type MLP as an integrator of ensemble based on hybrid methods” ensemble forecasting method incorporates an original combination of predictors. Predictions by this method have the lowest mean absolute error (MAE). In addition, this paper presents an original ensemble forecasting method, called “averaging ensemble based on hybrid methods without extreme forecasts”. Predictions by this method have the lowest root mean square error (RMSE) error among all tested methods. LSTM, a deep neural network, is the best single method, MLP is the second best one, while SVR, KNNR and, especially, linear regression (LR) perform less well. We prove that lagged values of forecasted time series slightly increase the accuracy of predictions. The same applies to seasonal and daily variability markers. Our studies have also demonstrated that using the full set of available input data and the best proposed hybrid and ensemble methods yield the lowest error. The proposed hybrid and ensemble methods are also applicable to other short-time generation forecasting in renewable energy sources (RES), e.g., in photovoltaic (PV) systems or hydropower.

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“…The technique improves traffic forecasting based on the Monte Carlo method [1]. For short-term forecasting, a few other network models using the Monte Carlo approach can be found in [29][30][31]. Narmanlioglu [12], Open RAN technical report [22,24,25] Gavrilovska et al [13] and Niknam et al [23] The open radio access network is a multi-vendor, interoperable product.…”

Section: Related Workmentioning

confidence: 99%

“…Narmanlioglu [12], Open RAN technical report [22,24,25] Gavrilovska et al [13] and Niknam et al [23] The open radio access network is a multi-vendor, interoperable product. This intelligently opens the protocols and interfaces between various RAN components to integrate various operators' networks and supports different deployment scenarios with lower cost and time to market Demand forecasting Sciancalepore et al [26,27], and Raikwar et al [28] The authors implemented Holt-Winters theory for longand short-term traffic forecasting to ensure efficient admission control and resource management in cellular networks Tseliou et al [1], Dudek et al [29,30], and Hippert et al [31] Monte Carlo-based prediction frameworks are proposed by the authors for on-demand resource allocation in cellular and neural networks Narmanlioglu et al [32], Miao et al [33], and Zhang et al [34] Bayesian techniques are adopted by the authors to predict the number of active users and their distribution within the cellular network for localization and resource allocation over handover Fuzzy-logic-based network selection and resources allocation Inaba et al [35], Bouali et al [18], Goudarzi et al [36], and Kaloxylos et al [37] The authors implemented the fuzzy-logic approach in their proposed hybrid model for an efficient access network selection among heterogeneous networks Khan et al [38], Zeng et al [39], silva et al [40], and Shrimali et al [41] The authors adopted fuzzy-logic and multi-criterion optimization schemes, or algorithms such as a genetic algorithm, to propose their framework for resource allocation in 5G cellular and vehicular networks [34] to predict the spatiotemporal information of traffic distribution in a cellular network. Holt-Winters and Bayesian are basic exponential smoothing techniques.…”

Section: Related Workmentioning

confidence: 99%

Dynamic traffic forecasting and fuzzy-based optimized admission control in federated 5G-open RAN networks

Perveen

Abozariba

Patwary

et al. 2021

Neural Comput & Applic

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Providing connectivity to high-density traffic demand is one of the key promises of future wireless networks. The open radio access network (O-RAN) is one of the critical drivers ensuring such connectivity in heterogeneous networks. Despite intense interest from researchers in this domain, key challenges remain to ensure efficient network resource allocation and utilization. This paper proposes a dynamic traffic forecasting scheme to predict future traffic demand in federated O-RAN. Utilizing information on user demand and network capacity, we propose a fully reconfigurable admission control framework via fuzzy-logic optimization. We also perform detailed analysis on several parameters (user satisfaction level, utilization gain, and fairness) over benchmarks from various papers. The results show that the proposed forecasting and fuzzy-logic-based admission control framework significantly enhances fairness and provides guaranteed quality of experience without sacrificing resource utilization. Moreover, we have proven that the proposed framework can accommodate a large number of devices connected simultaneously in the federated O-RAN.

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Multilayer perceptron for short-term load forecasting: from global to local approach

Cited by 55 publications

References 37 publications

Forecasting of 10-Second Power Demand of Highly Variable Loads for Microgrid Operation Control

Forecasting of 10-Second Power Demand of Highly Variable Loads for Microgrid Operation Control

Hybrid and Ensemble Methods of Two Days Ahead Forecasts of Electric Energy Production in a Small Wind Turbine

Dynamic traffic forecasting and fuzzy-based optimized admission control in federated 5G-open RAN networks

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