Multiregion Short-Term Load Forecasting in Consideration of HI and Load/Weather Diversity

Chu, Wen-Chen; Xu, Zheng-Wei; Lee, Wei-Jen

doi:10.1109/tia.2010.2090440

Cited by 32 publications

(18 citation statements)

References 9 publications

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“…Some other particular examples of forecasting in large areas with specific information about consumption values are the following ones: Chu et al [31] handle values of 33000 MW to forecast the peak load; Wang et al [32] manage high consumption in two large areas of China; on the other hand, Rejc and Partos [33] predict consumption between 1000 and 1500 MW in Slovenia, and Kebriaei et al [34] show a large area of Iran with an approximate consumption of 1550 MW. Therefore, it was found necessary to extend this knowledge domain in order to study forecasting in smaller and less-aggregated environments which might have higher variability in the demand curve.…”

Section: Related Workmentioning

confidence: 99%

Artificial neural networks for short-term load forecasting in microgrids environment

Hernández

Baladrón

Aguiar

et al. 2014

Energy

189

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The adaptation of energy production to demand has been traditionally very important for utilities in order to optimize resource consumption. This is especially true also in microgrids where many intelligent elements have to adapt their behaviour depending on the future generation and consumption conditions. However, traditional forecasting has been performed only for extremely large areas, such as nations and regions. This work aims at presenting a solution for Short-Term Load Forecasting (STLF) in microgrids, based on a three-stage architecture which starts with pattern recognition by a Self-Organizing Map (SOM), a clustering of the previous partition via kmeans algorithm, and finally demand forecasting for each cluster with a MultiLayer Perceptron.Model validation was performed with data from a microgrid-sized environment provided by the Spanish company Iberdrola.

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Section: Related Workmentioning

confidence: 99%

Artificial neural networks for short-term load forecasting in microgrids environment

Hernández

Baladrón

Aguiar

et al. 2014

Energy

189

View full text Add to dashboard Cite

show abstract

“…Taylor et al [55] present load forecasts for England and Wales, with 30,000-45,000 MW consumption. In Chu et al [56], the Taiwan Power Company (Taipower)-through Heat Index (HI)-perform peak load forecasting with values over 33,000 MW. In [51,57,58], the chosen areas for load forecasting are large provinces, which present high electric power consumption.…”

Section: Geographical Area In Load Forecastingmentioning

confidence: 99%

“…While the solutions studied in the literature [35][36][37][38][39][40][41][42][43][44][45][46][47][48][49][50][51][52][53][55][56][57][58][59] present sometimes good prediction efficiency figures (normally their MAPEs are around 2%), they deal almost exclusively with big areas, and mainly entire countries, and they are never applied to smaller environments of the size of small cities or microgrids. Therefore, they do not give any evidence of how will they behave when applied to highly variable load curves.…”

Section: Geographical Area In Load Forecastingmentioning

confidence: 99%

Short-Term Load Forecasting for Microgrids Based on Artificial Neural Networks

et al. 2013

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Received : 28 November 2012; in revised form: 18 February 2013 / Accepted: 20 February 2013 / Published: 5 March 2013 Abstract: Electricity is indispensable and of strategic importance to national economies. Consequently, electric utilities make an effort to balance power generation and demand in order to offer a good service at a competitive price. For this purpose, these utilities need electric load forecasts to be as accurate as possible. However, electric load depends on many factors (day of the week, month of the year, etc.), which makes load forecasting quite a complex process requiring something other than statistical methods. This study presents an electric load forecast architectural model based on an Artificial Neural Network (ANN) that performs Short-Term Load Forecasting (STLF). In this study, we present the excellent results obtained, and highlight the simplicity of the proposed model. Load forecasting was performed in a geographic location of the size of a potential microgrid, as microgrids appear to be the future of electric power supply.

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“…• decomposition into geographical regions due to distinct climate characteristics and consumer bases. In [31], the load forecasting in the four regions of Taiwan is independently performed using NNs.…”

Section: Introductionmentioning

confidence: 99%

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

Dudek

2019

Neural Comput & Applic

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Many forecasting models are built on neural networks. The key issues in these models, which strongly translate into the accuracy of forecasts, are data representation and the decomposition of the forecasting problem. In this work, we consider both of these problems using short-term electricity load demand forecasting as an example. A load time series expresses both the trend and multiple seasonal cycles. To deal with multi-seasonality, we consider four methods of the problem decomposition. Depending on the decomposition degree, the problem is split into local subproblems which are modeled using neural networks. We move from the global model, which is competent for all forecasting tasks, through the local models competent for the subproblems, to the models built individually for each forecasting task. Additionally, we consider different ways of the input data encoding and analyze the impact of the data representation on the results. The forecasting models are examined on the real power system data from four European countries. Results indicate that the local approaches can significantly improve the accuracy of load forecasting, compared to the global approach. A greater degree of decomposition leads to the greater reduction in forecast errors.

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Multiregion Short-Term Load Forecasting in Consideration of HI and Load/Weather Diversity

Cited by 32 publications

References 9 publications

Artificial neural networks for short-term load forecasting in microgrids environment

Artificial neural networks for short-term load forecasting in microgrids environment

Short-Term Load Forecasting for Microgrids Based on Artificial Neural Networks

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

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