Combined modeling for electric load forecasting with adaptive particle swarm optimization

Wang, Jianzhou; Zhu, Suling; Zhang, Wenyu; Lu, Hanqing

doi:10.1016/j.energy.2009.12.015

Cited by 120 publications

(53 citation statements)

References 27 publications

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“…It is observed that ARIMA is one of the most used linear prediction techniques. For instance, Wang et al studied electricity price estimation with Winters' exponential smoothing and SARIMA methods [39].…”

Section: Related Workmentioning

confidence: 99%

Year Ahead Demand Forecast of City Natural Gas Using Seasonal Time Series Methods

Akpınar

Yumuşak

2016

Energies

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Consumption of natural gas, a major clean energy source, increases as energy demand increases. We studied specifically the Turkish natural gas market. Turkey's natural gas consumption increased as well in parallel with the world's over the last decade. This consumption growth in Turkey has led to the formation of a market structure for the natural gas industry. This significant increase requires additional investments since a rise in consumption capacity is expected. One of the reasons for the consumption increase is the user-based natural gas consumption influence. This effect yields imbalances in demand forecasts and if the error rates are out of bounds, penalties may occur. In this paper, three univariate statistical methods, which have not been previously investigated for mid-term year-ahead monthly natural gas forecasting, are used to forecast natural gas demand in Turkey's Sakarya province. Residential and low-consumption commercial data is used, which may contain seasonality. The goal of this paper is minimizing more or less gas tractions on mid-term consumption while improving the accuracy of demand forecasting. In forecasting models, seasonality and single variable impacts reinforce forecasts. This paper studies time series decomposition, Holt-Winters exponential smoothing and autoregressive integrated moving average (ARIMA) methods. Here, 2011-2014 monthly data were prepared and divided into two series. The first series is 2011-2013 monthly data used for finding seasonal effects and model requirements. The second series is 2014 monthly data used for forecasting. For the ARIMA method, a stationary series was prepared and transformation process prior to forecasting was done. Forecasting results confirmed that as the computation complexity of the model increases, forecasting accuracy increases with lower error rates. Also, forecasting errors and the coefficients of determination values give more consistent results. Consequently, when there is only consumption data in hand, all methods provide satisfying results and the differences between each method is very low. If a statistical software tool is not used, time series decomposition, the most primitive method, or Winters exponential smoothing requiring little mathematical knowledge for natural gas demand forecasting can be used with spreadsheet software. A statistical software tool containing ARIMA will obtain the best results.

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Section: Related Workmentioning

confidence: 99%

Year Ahead Demand Forecast of City Natural Gas Using Seasonal Time Series Methods

Akpınar

Yumuşak

2016

Energies

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“…The main methods include monotone iterative algorithm [38], evolutionary programming [39], and quadratic programming [40]. Wang et al [41] propose using adaptive practical swarm optimization algorithm to optimize the weight of the integrated model. The second kind is to determine the weights by evaluating the algorithm's score.…”

Section: Related Workmentioning

confidence: 99%

Predicting Short‐Term Electricity Demand by Combining the Advantages of ARMA and XGBoost in Fog Computing Environment

Zheng

Yang

et al. 2018

Wireless Communications and Mobile Computing

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With the rapid development of IoT, the disadvantages of Cloud framework have been exposed, such as high latency, network congestion, and low reliability. Therefore, the Fog Computing framework has emerged, with an extended Fog Layer between the Cloud and terminals. In order to address the real-time prediction on electricity demand, we propose an approach based on XGBoost and ARMA in Fog Computing environment. By taking the advantages of Fog Computing framework, we first propose a prototypebased clustering algorithm to divide enterprise users into several categories based on their total electricity consumption; we then propose a model selection approach by analyzing users' historical records of electricity consumption and identifying the most important features. Generally speaking, if the historical records pass the test of stationarity and white noise, ARMA is used to model the user's electricity consumption in time sequence; otherwise, if the historical records do not pass the test, and some discrete features are the most important, such as weather and whether it is weekend, XGBoost will be used. The experiment results show that our proposed approach by combining the advantage of ARMA and XGBoost is more accurate than the classical models.

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“…A combined model based on data pre-analysis was proposed for electrical load forecasting and cuckoo search algorithm was applied to optimize the weight coefficients [25]. A combined model has been developed for electric load forecasting and adaptive particle swarm optimization (APSO) algorithm was used to determine the weight coefficients allocated to each individual model [26]. Hybrid model has also been put into several different models and makes full use of the information of every model.…”

Section: Introductionmentioning

confidence: 99%

“…Through analyzing the published research in short-term load forecasting we could find that the load data are usually divided into different data type to predict. For example, a week was divided into two types [26], working days (Monday to Friday) and rest days (Saturday and Sunday), or into seven types [28] which take every day of a week as a type. All of these methods are not flexible, so this paper proposed a new flexible load classification model which took cycle of load data into account.…”

Section: Introductionmentioning

confidence: 99%

“…There is a very close relationship between the precision of artificial neural network and the selection of ANN's parameters such as weights. To improve the accuracy, some optimal algorithm techniques were applied, such as PSO (Particle Swarm Optimization) [33,34], APSO (Adaptive Particle Swarm Optimization) [26], DE (Differential Evolution) [35], GA (Genetic Algorithms) [36], BFT (Bacterial Foraging Technique) [32] and high-order Markov chain model [37]. Evolutionary computation algorithm (EC), the most famous optimal algorithm, was also used to tune the connection weights and the parameters of dilation and translation in the WNN [38].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Hybrid Model Equipped with the Minimum Cycle Decomposition Concept for Short-Term Forecasting of Electrical Load Time Series

Shen³

et al. 2017

Neural Process Lett

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Electricity load forecasting is an essential, however complicated work. Due to the influence of a large number of uncertain factors, it shows complicated nonlinear combination features. Therefore, it is difficult to improve the prediction accuracy and the tremendous breadth of applicability especially for using a single method. In order to improve the performance including accuracy and applicability of electricity load forecasting, in this paper, a concept named minimum cycle decomposition (MCD) that the raw data are grouped according to the minimum cycle was proposed for the first time. In addition, a hybrid prediction model (HMM) based on one-order difference, ensemble empirical model decomposition (EEMD), mind evolutionary computation (MEC) and wavelet neural network (WNN) was also proposed in this study. The HMM model consists of two parts. Part one, pre-processing, known as one order difference to remove the trend of subsequence and EEMD to reduce the noise, was performed by HMM model on each subset. Part two, the WNN optimized by MEC (WNN + MEC) was applied on resultant subseries. Finally, a number of different models were used as the comparative experiment to validate the effectiveness of the presented method, such as back propagation neural network (BP-1), BPNN combined MCD (BP-2), WNN combined MCD (WNNM), a HMM (DEEPLSSVM) based on one-order difference, EEMD, particle swarm optimization and least squares support vector machine and a hybrid model (DEESGRNN) based on one-order difference, EEMD, simulate anneal and generalized regression neural network. Certain evaluation measurements are taken into account to assess the performance. Experiments were carried out on QLD (Queensland) and NSW (New South Wales) electricity markets historical data, and the experimental results show that the MCD has the advantages of improving model accuracy and of generalization ability. In addition, the simulation results also suggested that the proposed hybrid model has better performance. B Zhaoshuang He

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Combined modeling for electric load forecasting with adaptive particle swarm optimization

Cited by 120 publications

References 27 publications

Year Ahead Demand Forecast of City Natural Gas Using Seasonal Time Series Methods

Year Ahead Demand Forecast of City Natural Gas Using Seasonal Time Series Methods

Predicting Short‐Term Electricity Demand by Combining the Advantages of ARMA and XGBoost in Fog Computing Environment

A Hybrid Model Equipped with the Minimum Cycle Decomposition Concept for Short-Term Forecasting of Electrical Load Time Series

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