A Permutation Importance-Based Feature Selection Method for Short-Term Electricity Load Forecasting Using Random Forest

Huang, Nantian; Lu, Guobo; Xu, Dianguo

doi:10.3390/en9100767

Cited by 121 publications

(68 citation statements)

References 30 publications

(27 reference statements)

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“…This chapter is divided into 2 parts: data description and experimental results. To demonstrate completely the performance of WindNet proposed in this paper, this chapter will also include comparisons of very popular and commonly used machine learning algorithms, such as support vector machine (SVM) [33][34][35][36][37][38], random forest (RF) [39][40][41][42][43][44], decision tree (DT) [45][46][47][48][49][50] and MLP.…”

Section: Resultsmentioning

confidence: 99%

A Short-Term Wind Speed Forecasting Model by Using Artificial Neural Networks with Stochastic Optimization for Renewable Energy Systems

Huang

Kuo

2018

Energies

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To efficiently manage unstable wind power generation, precise short-term wind speed forecasting is critical. To overcome the challenges in wind speed forecasting, this paper proposes a new convolutional neural network algorithm for short-term forecasting. In this paper, the forecasting performance of the proposed algorithm was compared to that of four other artificial intelligence algorithms commonly used in wind speed forecasting. Numerical testing results based on data from a designated wind site in Taiwan were used to demonstrate the efficiency of above-mentioned proposed learning method. Mean absolute error (MAE) and root-mean-square error (RMSE) were adopted as accuracy evaluation indexes in this paper. Experimental results indicate that the MAE and RMSE values of the proposed algorithm are 0.800227 and 0.999978, respectively, demonstrating very high forecasting accuracy.

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Section: Resultsmentioning

confidence: 99%

A Short-Term Wind Speed Forecasting Model by Using Artificial Neural Networks with Stochastic Optimization for Renewable Energy Systems

Huang

Kuo

2018

Energies

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“…In order to fully demonstrate the performance of the EPNet proposed in this paper, this chapter includes comparisons between Support Vector Machine (SVM) [25][26][27][28][29][30], Random Forest (RF) [31][32][33][34][35][36], Decision Tree (DT) [37][38][39][40][41][42], MLP, CNN and LSTM. Figure 6 is the Electric Power Markets (PJM) Regulation Zone Preliminary Billing Data [43] used in this experiment, this data records the regulation market capacity clearing price of every half hour in 2017.…”

Section: Resultsmentioning

confidence: 99%

An Electricity Price Forecasting Model by Hybrid Structured Deep Neural Networks

2018

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Electricity price is a key influencer in the electricity market. Electricity market trades by each participant are based on electricity price. The electricity price adjusted with the change in supply and demand relationship can reflect the real value of electricity in the transaction process. However, for the power generating party, bidding strategy determines the level of profit, and the accurate prediction of electricity price could make it possible to determine a more accurate bidding price. This cannot only reduce transaction risk, but also seize opportunities in the electricity market. In order to effectively estimate electricity price, this paper proposes an electricity price forecasting system based on the combination of 2 deep neural networks, the Convolutional Neural Network (CNN) and the Long Short Term Memory (LSTM). In order to compare the overall performance of each algorithm, the Mean Absolute Error (MAE) and Root-Mean-Square error (RMSE) evaluating measures were applied in the experiments of this paper. Experiment results show that compared with other traditional machine learning methods, the prediction performance of the estimating model proposed in this paper is proven to be the best. By combining the CNN and LSTM models, the feasibility and practicality of electricity price prediction is also confirmed in this paper.

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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%

“…Other techniques include fuzzy logic [10] and expert systems [11]. In [12], a novel methodology based in random forest is used to improve feature selection and enhance prediction accuracy. Support Vector Regression (SVR) has been proposed as a feasible alternative to ANNs.…”

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 Permutation Importance-Based Feature Selection Method for Short-Term Electricity Load Forecasting Using Random Forest

Cited by 121 publications

References 30 publications

A Short-Term Wind Speed Forecasting Model by Using Artificial Neural Networks with Stochastic Optimization for Renewable Energy Systems

A Short-Term Wind Speed Forecasting Model by Using Artificial Neural Networks with Stochastic Optimization for Renewable Energy Systems

An Electricity Price Forecasting Model by Hybrid Structured Deep Neural Networks

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

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