Short-Term Transmission-Loss Forecast for the Slovenian Transmission Power System Based on a Fuzzy-Logic Decision Approach

Rejc, Matej; Pantoš, Miloš

doi:10.1109/tpwrs.2010.2096829

Cited by 66 publications

(33 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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

193

View full text Add to dashboard Cite

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.

show abstract

Section: Related Workmentioning

confidence: 99%

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

Hernández

Baladrón

Aguiar

et al. 2014

Energy

193

View full text Add to dashboard Cite

show abstract

“…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. Rejc et al [52] apply a novel short-term active-power-loss forecast method for Slovenia, which has a consumption of 950-1550 MW.…”

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%

“…References [35][36][37][38][39] and [49,50] offer generally short prediction horizons, normally forecasting values in the next hour. While this work employs 29 input variables and 16 neurons in the hidden layer, [44,[51][52][53] use high dimensional input spaces (with a number of input variables ranging from 40 and 50 and neurons in the hidden layer between 24 and 50) and therefore require a bigger training database to reach similar results.…”

Section: Comparison With Other Solutionsmentioning

confidence: 99%

“…References [51][52][53] describe complex models capable of hourly prediction 24-hour in advance, but they use between 40 and 50 input variables and a hidden layer with a number of neurons ranging from 24 to 50. A similar case is presented in [44], where 30-minute predictions are provided 24 h in advance, but using more than 50 input variables.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

et al. 2013

View full text Add to dashboard Cite

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.

show abstract

An effective deep learning neural network model for short‐term load forecasting

Wang

et al. 2020

Concurrency and Computation

View full text Add to dashboard Cite

Summary Energy load forecasting plays an important role in the smart grid, which can affect the promoting energy production and consumption decision‐making processes. In this paper, the state‐of‐the‐art deep learning (DL) neural models are used in the short‐term load forecasting, including the multilayer perceptron (MLP), the convolutional neural network (CNN), and the long short‐term memory (LSTM). A novel loss function is proposed for the load forecasting, and two commonly used benchmarks are used to verify the validity of the proposed function. The simulation results show that the mean absolute percentage error (MAPE) of the proposed loss function is 19.63% lower than cross‐entropy and 2.34% lower than mean absolute error (MAE). We compared the mentioned neural networks in different aspects, and the results show that in energy load forecasting, CNN has superior performance than MLP and LSTM in terms of high accuracy and robustness to weather changes.

show abstract

Short-Term Transmission-Loss Forecast for the Slovenian Transmission Power System Based on a Fuzzy-Logic Decision Approach

Cited by 66 publications

References 13 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

An effective deep learning neural network model for short‐term load forecasting

Contact Info

Product

Resources

About