Research and Application of Hybrid Forecasting Model Based on an Optimal Feature Selection System—A Case Study on Electrical Load Forecasting

Dong, Yunxuan; Wang, Jianzhou; Wang, Chen; Guo, Zhengfu

doi:10.3390/en10040490

Cited by 14 publications

(6 citation statements)

References 92 publications

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“…Today, MTLF is a method used in the smart grid for electricity price and load forecasting, although it has not gained full explorations. Contrarily, STLF is widely studied in [ 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 24 ], which imperatively introduce competitiveness in the electricity markets. Several methods of load forecasting begin from the conventional time series analysis to computational intelligence such as machine learning.…”

Section: Related Workmentioning

confidence: 99%

“…The accuracy of the proposed forecasting model is examined using cumulative variation of root mean square error (RMSE) and mean absolute percentage error. Dong et al [ 13 ] demonstrated STLF via a unit feature learning known as pyramid system and recurrent neural network (PRNN). The proposed system provides security and stability of the power grid.…”

Section: Related Workmentioning

confidence: 99%

“…Note that FS is a method used in selecting the important feature variables that are necessary and efficient to train a forecasting model. Other applications of FS methods in STLF and VSTLF are given in [ 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 ]. Efficient FS methods enhance forecasting accuracy and make the forecasting model faster to train with less complexity.…”

Section: Introductionmentioning

confidence: 99%

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Towards Modified Entropy Mutual Information Feature Selection to Forecast Medium-Term Load Using a Deep Learning Model in Smart Homes

Samuel

Al-Zahrani

Khan

et al. 2020

Entropy

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Over the last decades, load forecasting is used by power companies to balance energy demand and supply. Among the several load forecasting methods, medium-term load forecasting is necessary for grid’s maintenance planning, settings of electricity prices, and harmonizing energy sharing arrangement. The forecasting of the month ahead electrical loads provides the information required for the interchange of energy among power companies. For accurate load forecasting, this paper proposes a model for medium-term load forecasting that uses hourly electrical load and temperature data to predict month ahead hourly electrical loads. For data preprocessing, modified entropy mutual information-based feature selection is used. It eliminates the redundancy and irrelevancy of features from the data. We employ the conditional restricted Boltzmann machine (CRBM) for the load forecasting. A meta-heuristic optimization algorithm Jaya is used to improve the CRBM’s accuracy rate and convergence. In addition, the consumers’ dynamic consumption behaviors are also investigated using a discrete-time Markov chain and an adaptive k-means is used to group their behaviors into clusters. We evaluated the proposed model using GEFCom2012 US utility dataset. Simulation results confirm that the proposed model achieves better accuracy, fast convergence, and low execution time as compared to other existing models in the literature.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Towards Modified Entropy Mutual Information Feature Selection to Forecast Medium-Term Load Using a Deep Learning Model in Smart Homes

Samuel

Al-Zahrani

Khan

et al. 2020

Entropy

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“…This model can significantly enhance the effectiveness of power load forecast. Dong, Y. et al [26] developed a short-term load prediction model using a unit for feature learning named Pyramid System and recurrent neural networks, and it can greatly increase the stabilization and safety of the smart grid. Wang, R. et al [27] proposed a new power load forecasting system by combining data preprocessing, hybrid optimized algorithm and certain individual conventional prediction methods, which conquers the shortcomings of individual conventional prediction model and obtains a single model optimization with higher prediction accuracy than traditional forecasting model.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Double Forecasting System of Short Term Power Load Based on Swarm Intelligence and Nonlinear Integration Mechanism

Jiang¹,

Nie²

2020

Applied Sciences

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Accurate and reliable power load forecasting not only takes an important place in management and steady running of smart grid, but also has environmental benefits and economic dividends. Accurate load point forecasting can provide a guarantee for the daily operation of the power grid, and effective interval forecasting can further quantify the uncertainty of power load on this basis to provide dependable and precise load information. However, most of the previous work focuses on the deterministic point prediction of power load and rarely considers the interval prediction of power load, which makes the prediction of power load not comprehensive. In this study, a new double hybrid load forecasting system including point forecasting module and interval forecasting module is developed, which can make up for the shortcomings of incomplete analysis for the existing research. The point forecasting module adopts a nonlinear integration mechanism based on Back Propagation (BP) network optimized by Multi-objective Evolutionary Algorithm based on Decomposition (MOEA/D) to improve the accuracy of point prediction. A fuzzy clustering interval prediction method based on different data feature classification is successfully proposed which provides an effective tool for load uncertainty analysis. The experiment results show that the system not only has a good effect in accurately predicting power load, but also can analyze the uncertainty of the power load, which can be used as an effective technology of power system planning.

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“…Since estimating the electricity demand becomes harder as the planning horizon increases, the predictions can be strongly influenced by several nonuniform variables such as electric consumption, temperature, air humidity, and socioeconomic aspects. Moreover, long-and regular-term time series make the problem more difficult to be technically managed and solved, as obtaining a computationally robust solution to act in real scenarios requires the integration of customized tuning approaches and non-linear models as a unified framework to properly work [15][16][17][18][19]. Therefore, in this paper, our main interest lies in designing well-behaved forecasters to assess and predict the electricity demand in Brazil for both long-and regular-term time series.Considering the recent advances in Machine Learning (ML) for electricity load forecasting, the literature offers a variety of approaches, most of them specifically designed to solve a particular case study of energy consumption.…”

mentioning

confidence: 99%

Towards Assessing the Electricity Demand in Brazil: Data-Driven Analysis and Ensemble Learning Models

et al. 2020

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The prediction of electricity generation is one of the most important tasks in the management of modern energy systems. Improving the assertiveness of this prediction can support government agencies, electric companies, and power suppliers in minimizing the electricity cost to the end consumer. In this study, the problem of forecasting the energy demand in the Brazilian Interconnected Power Grid was addressed, by gathering different energy-related datasets taken from public Brazilian agencies into a unified and open database, used to tune three machine learning models. In contrast to several works in the Brazilian context, which provide only annual/monthly load estimations, the learning approaches Random Forest, Gradient Boosting, and Support Vector Machines were trained and optimized as new ensemble-based predictors with parameter tuning to reach accurate daily/monthly forecasts. Moreover, a detailed and in-depth exploration of energy-related data as obtained from the Brazilian power grid is also given. As shown in the validation study, the tuned predictors were effective in producing very small forecasting errors under different evaluation scenarios.Techniques devoted to forecasting electricity demand aim at estimating the amount of energy needed, over a historical time series, for transmission and later consumption by others. Despite the adaptation of several machine learning models to properly address the problem [5-9], tracking the progressive use of the electricity is not a straightforward task in practice. In fact, the electricity dispatch is intrinsically related to the internal operations of the power systems such as the periodic scheduling of power generation in hydroelectric plants, the preventive maintenance of the generators, the reliability evaluation of power systems, etc. [10]. Moreover, the problem becomes even more challenging and especially interesting when one has to deal with the highly nonlinear tendency of power data, as it is mathematically modeled by highly-oscillating time series whose parameters can be affected by exogenous variables such as weather/ambient conditions [11] and economy-related factors [12].Formally, the problem of predicting the power demand on time series can be described as follows: given a time series of electric load X 1 , X 2 , . . . , X t , in which X i accounts for the historical energy load at the instant i, i = 1, 2, ..., t, the goal is to predict the quantity X (t+h) , where h establishes the forecast horizon [13,14]. Taxonomically speaking, this kind of prediction usually comprises three categories of planning horizons: (i) long-term (years/months); (ii) regular-term (days/weeks); and (iii) short-term (minutes/hours). Since estimating the electricity demand becomes harder as the planning horizon increases, the predictions can be strongly influenced by several nonuniform variables such as electric consumption, temperature, air humidity, and socioeconomic aspects. Moreover, long-and regular-term time series make the problem more difficult to be technically manag...

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Research and Application of Hybrid Forecasting Model Based on an Optimal Feature Selection System—A Case Study on Electrical Load Forecasting

Cited by 14 publications

References 92 publications

Towards Modified Entropy Mutual Information Feature Selection to Forecast Medium-Term Load Using a Deep Learning Model in Smart Homes

Towards Modified Entropy Mutual Information Feature Selection to Forecast Medium-Term Load Using a Deep Learning Model in Smart Homes

A Hybrid Double Forecasting System of Short Term Power Load Based on Swarm Intelligence and Nonlinear Integration Mechanism

Towards Assessing the Electricity Demand in Brazil: Data-Driven Analysis and Ensemble Learning Models

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