Some Aspects Concerning Mid Term Monthly Load Forecasting Using ANN

Borlea, Ioan-Daniel; Buta, A.; Lustrea, B.

doi:10.1109/eurcon.2005.1629908

Cited by 4 publications

(4 citation statements)

References 7 publications

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“…Mid-term load forecasting is especially critical to enforcing a reliable power system in a smart city by making it possible to generate electricity depending on the future energy demand [24]. For instance, Seoul, which is the capital and largest metropolis in South Korea, has been pursuing a leading smart city, which requires an accurate monthly electric load forecasting model for annual urban energy planning [25,26].…”

Section: Introductionmentioning

confidence: 99%

Monthly Electric Load Forecasting Using Transfer Learning for Smart Cities

et al. 2020

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Monthly electric load forecasting is essential to efficiently operate urban power grids. Although diverse forecasting models based on artificial intelligence techniques have been proposed with good performance, they require sufficient datasets for training. In the case of monthly forecasting, because just one data point is generated per month, it is not easy to collect sufficient data to construct models. This lack of data can be alleviated using transfer learning techniques. In this paper, we propose a novel monthly electric load forecasting scheme for a city or district based on transfer learning using similar data from other cities or districts. To do this, we collected the monthly electric load data from 25 districts in Seoul for five categories and various external data, such as calendar, population, and weather data. Then, based on the available data of the target city or district, we selected similar data from the collected datasets by calculating the Pearson correlation coefficient and constructed a forecasting model using the selected data. Lastly, we fine-tuned the model using the target data. To demonstrate the effectiveness of our model, we conducted an extensive comparison with other popular machine-learning techniques through various experiments. We report some of the results.

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Section: Introductionmentioning

confidence: 99%

Monthly Electric Load Forecasting Using Transfer Learning for Smart Cities

et al. 2020

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“…There are three types of load forecasting: short-term load forecasting which consist in forecasting the load demand curve from 1 hour, 24 hour to one week ahead, mid-term load forecasting which consist in forecasting the load demand curve from 1 week to 1 year, and long-term load forecasting which consist in forecasting the load demand from 1 year to 20 years ahead. For doing these tasks, artificial neural networks have been widely used in the last decade due to their power to recognize patterns even with noisy data [3], [5], [6], [9], [1], [2]. Nevertheless, there are still two problems for using neural networks in load forecasting: the first problem is the poor load forecasting in holidays due to the complex behavior of load in holidays and the lack of samples to train correctly the neural network.…”

Section: Introductionmentioning

confidence: 99%

Daily Load Forecasting Using Quick Propagation Neural Network with a Special Holiday Encoding

Aquino

Perez

Chavez

et al. 2007

2007 International Joint Conference on Neural Networks

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In the last decade, neural networks have been applied in Daily Load Forecasting. Nevertheless, two main problems are still present for using neural networks in this domain: first, poor load forecasting in holidays because complex load behavior, and second, the lack of a global model for both holidays and non-holidays. To solve these two problems, we propose a new special holiday encoding that considers holidays and its preceding and following days which are also affected by the holiday. This proposed encoding is used in conjunction with quick propagation neural network. In the experiments the proposed holiday encoding is compared with other encoding based on the forecasting error of quick propagation. To evaluate their performances, we used a Peruvian load data set. The results show that the proposed holiday encoding produce better forecasting results than the results produced by other holiday encoding. Finally, these same results are also better than those results obtained by using ARIMA model which is a statistical technique also used in practice.

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“…2) Artificial Neural Network (ANN) [34][35][36][37][38] used Artificial Neural Network (ANN) approach to forecast electrical demand load, by using the data supporting from the government. The forecasting can be performed the results in yearly (to 15 years), weekly (to 3 years) and hourly (to 24 hours).…”

Section: B Artificial Intelligence Technology Methods and Other Methmentioning

confidence: 99%

“…Historical load inputs and meteorological data such as monthly maximum temperature, minimum temperature are used in [35][36][37] and [39,40]. Economic variables are also included in [38].…”

Section: A Data Inputs 1) I Nput Classificationmentioning

confidence: 99%

A Computing Model of Artificial Intelligent Approaches to Mid-term Load Forecasting: a state-of-the-art- survey for the researcher

Bunnoon¹,

Chalermyanont²,

Limsakul³

2010

IJET

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Abstract-This article presents the review of the computing models applied for solving problems of midterm load forecasting. The load forecasting results can be used in electricity generation such as energy reservation and maintenance scheduling. Principle, strategy and results of short term, midterm, and long term load forecasting using statistic methods and artificial intelligence technology (AI) are summaried, Which, comparison between each method and the articles have difference feature input and strategy. The last, will get the idea or literature review conclusion to solve the problem of mid term load forecasting (MTLF).

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Some Aspects Concerning Mid Term Monthly Load Forecasting Using ANN

Cited by 4 publications

References 7 publications

Monthly Electric Load Forecasting Using Transfer Learning for Smart Cities

Monthly Electric Load Forecasting Using Transfer Learning for Smart Cities

Daily Load Forecasting Using Quick Propagation Neural Network with a Special Holiday Encoding

A Computing Model of Artificial Intelligent Approaches to Mid-term Load Forecasting: a state-of-the-art- survey for the researcher

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