A hybrid short-term load forecasting with a new data preprocessing framework

Ghayekhloo, M.; Menhaj, Mohammad Bagher; Ghofrani, M.

doi:10.1016/j.epsr.2014.09.002

Cited by 61 publications

(22 citation statements)

References 21 publications

(31 reference statements)

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“…Namely, inefficient storage and discharge of electricity could incur unnecessary costs, while even a small improvement in electricity load forecasting could reduce production costs and increase trading advantages [4], particularly during the peak electricity consumption periods. Therefore, it is important for electricity providers to model and forecast electricity load as accurately as possible, in both short-term [5][6][7][8][9][10][11][12] (one day to one month ahead) and medium-term [13] (one month to five years ahead) periods.…”

Section: Introductionmentioning

confidence: 99%

A High Precision Artificial Neural Networks Model for Short-Term Energy Load Forecasting

2018

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Abstract:One of the most important research topics in smart grid technology is load forecasting, because accuracy of load forecasting highly influences reliability of the smart grid systems. In the past, load forecasting was obtained by traditional analysis techniques such as time series analysis and linear regression. Since the load forecast focuses on aggregated electricity consumption patterns, researchers have recently integrated deep learning approaches with machine learning techniques. In this study, an accurate deep neural network algorithm for short-term load forecasting (STLF) is introduced. The forecasting performance of proposed algorithm is compared with performances of five artificial intelligence algorithms that are commonly used in load forecasting. The Mean Absolute Percentage Error (MAPE) and Cumulative Variation of Root Mean Square Error (CV-RMSE) are used as accuracy evaluation indexes. The experiment results show that MAPE and CV-RMSE of proposed algorithm are 9.77% and 11.66%, respectively, displaying very high forecasting accuracy.

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Section: Introductionmentioning

confidence: 99%

A High Precision Artificial Neural Networks Model for Short-Term Energy Load Forecasting

2018

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“…Load forecasting algorithms can be divided into three major categories: traditional methods, modern intelligent methods and hybrid algorithms [1]. The traditional method [1,2] mainly includes autoregressive (AR), autoregressive moving average (ARMA) [3], autoregressive integrated moving average (ARIMA) [4], semi-parametric [5], gray model [6,7], similar-day models [8], and Kalman filtering method [9]. Due to the theoretical limitations of the algorithms themselves, it is difficult to improve the forecasting accuracy using these forecasting approaches.…”

Section: Introductionmentioning

confidence: 99%

Electric Load Forecasting Based on a Least Squares Support Vector Machine with Fuzzy Time Series and Global Harmony Search Algorithm

et al. 2016

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This paper proposes a new electric load forecasting model by hybridizing the fuzzy time series (FTS) and global harmony search algorithm (GHSA) with least squares support vector machines (LSSVM), namely GHSA-FTS-LSSVM model. Firstly, the fuzzy c-means clustering (FCS) algorithm is used to calculate the clustering center of each cluster. Secondly, the LSSVM is applied to model the resultant series, which is optimized by GHSA. Finally, a real-world example is adopted to test the performance of the proposed model. In this investigation, the proposed model is verified using experimental datasets from the Guangdong Province Industrial Development Database, and results are compared against autoregressive integrated moving average (ARIMA) model and other algorithms hybridized with LSSVM including genetic algorithm (GA), particle swarm optimization (PSO), harmony search, and so on. The forecasting results indicate that the proposed GHSA-FTS-LSSVM model effectively generates more accurate predictive results.

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“…The difference could be due to the use of electrical heating equipment in Network 1 [30] and also because of the typical load profile in household customers in Spain, which corresponds to peak values at 19-22 h (evening) and at 11-14 h (morning). The load pattern of commercial customers is heavily influenced by opening and closing hours (9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20)(21) and the use of cooling appliances during summer. …”

Section: Data Analysis and Pre-processingmentioning

confidence: 99%

“…In [14], the least-square version of SVM regression is used to forecast daily peak load demand. Hybrid load forecasting frameworks have been used lately, which combine both statistical approaches and artificial intelligence-based models [15]. In [16], a meta-learning system is proposed, which automatically selects the best load forecasting algorithm out of seven well-known options based on the similarity of the new samples with previously analyzed ones, considering not only univariate data, but also multivariate data.…”

Section: Introductionmentioning

confidence: 99%

Assessment of an Adaptive Load Forecasting Methodology in a Smart Grid Demonstration Project

et al. 2017

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This paper presents the implementation of an adaptive load forecasting methodology in two different power networks from a smart grid demonstration project deployed in the region of Madrid, Spain. The paper contains an exhaustive comparative study of different short-term load forecast methodologies, addressing the methods and variables that are more relevant to be applied for the smart grid deployment. The evaluation followed in this paper suggests that the performance of the different methods depends on the conditions of the site in which the smart grid is implemented. It is shown that some non-linear methods, such as support vector machine with a radial basis function kernel and extremely randomized forest offer good performance using only 24 lagged load hourly values, which could be useful when the amount of data available is limited due to communication problems in the smart grid monitoring system. However, it has to be highlighted that, in general, the behavior of different short-term load forecast methodologies is not stable when they are applied to different power networks and that when there is a considerable variability throughout the whole testing period, some methods offer good performance in some situations, but they fail in others. In this paper, an adaptive load forecasting methodology is proposed to address this issue improving the forecasting performance through iterative optimization: in each specific situation, the best short-term load forecast methodology is chosen, resulting in minimum prediction errors.

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A hybrid short-term load forecasting with a new data preprocessing framework

Cited by 61 publications

References 21 publications

A High Precision Artificial Neural Networks Model for Short-Term Energy Load Forecasting

A High Precision Artificial Neural Networks Model for Short-Term Energy Load Forecasting

Electric Load Forecasting Based on a Least Squares Support Vector Machine with Fuzzy Time Series and Global Harmony Search Algorithm

Assessment of an Adaptive Load Forecasting Methodology in a Smart Grid Demonstration Project

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