Special Issue "Machine Learning for Energy Forecasting"

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Energy".

Deadline for manuscript submissions: 31 July 2020.

Special Issue Editor

Prof. Dr. Kuo-Ping Lin
E-Mail Website
Guest Editor
Department of Industrial Engineering and Enterprise Information, Tunghai University, Taichung 40704, Taiwan
Tel. +886-4-23594319
Interests: artificial intelligent; machine learning; e-commence; fuzzy system; neuro-fuzzy systems; system optimization

Special Issue Information

Dear Colleagues,

This Special Issue aims to investigate the Machine Learning for Energy Forecasting. Energy forecasting is an essential component of the energy industry. Energy forecasting includes loading forecast, electricity price forecast, wind power forecast, and solar power forecast, etc. Not only can the accurate forecasting support investment profitability analysis and power generation planning, but it also enables smart strategies to be applied to price bidding and risk management. In addition, it can optimize the grid operation. However, building a reliable forecasting solution has always been challenging. The machine learning is one of techniques for energy forecasting. With Machine Learning Forecasting, processors learn from mining loads of energy data without human interference. Extrapolative analysis and algorithms include support vector machines (SVM), least square support vector machines (LSSVM), Recurrent Neural Networks (RNN), Bayesian Neural Network (BNN), CART regression trees, Gaussian Processes (GP), Generalized Regression Neural Networks (GRNN), and Multi-Layer Perceptron (MLP), etc. I am looking for new research based on novel machine learning techniques for Energy Forecasting.

Prof. Dr. Kuo-Ping Lin
Guest Editor

Manuscript Submission Information

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Keywords

  • Machine Learning
  • Energy Forecasting
  • Artificial Intelligence
  • Sustainable Energy

Published Papers (4 papers)

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Research

Open AccessArticle
Research on Short-Term Wind Power Forecasting by Data Mining on Historical Wind Resource
Appl. Sci. 2020, 10(4), 1295; https://doi.org/10.3390/app10041295 - 14 Feb 2020
Abstract
In order to enhance the accuracy of short-term wind power forecasting (WPF), a short-term wind power forecasting method based on historical wind resources by data mining has been designed. Firstly, the spoiled data resulting from wind turbine and meteorological monitoring equipment is eliminated, [...] Read more.
In order to enhance the accuracy of short-term wind power forecasting (WPF), a short-term wind power forecasting method based on historical wind resources by data mining has been designed. Firstly, the spoiled data resulting from wind turbine and meteorological monitoring equipment is eliminated, and the missing data is added by the Lomnaofski optimization model, which is based on the temporal-spatial correlation of meteorological data. Secondly, the wind characteristics are analyzed by the continuous time similarity clustering (CTSC) method, which is used to select similar samples. To improve the accuracy of deterministic prediction and prediction error, the radial basis function neural network (RBF) deterministic forecasting model was built, which can approximate nonlinear solutions. In addition, the wind power interval prediction method, combining fuzzy information granulation and an Elman neural network (FIG-Elman), is proposed to acquire forecasting intervals. The deterministic prediction of the RBF-CTSC model has high accuracy, which can accurately describe the randomness, fluctuation and nonlinear characteristics of wind speed. Additionally, the mean absolute error (MAE) and root mean square error (RMSE) are reduced by the new model. The interval prediction of FIG-Elman results show that the interval width decreased by 18.85%, and the coverage probability of interval increased by 10.94%. Full article
(This article belongs to the Special Issue Machine Learning for Energy Forecasting)
Open AccessArticle
Energy Performance Forecasting of Residential Buildings Using Fuzzy Approaches
Appl. Sci. 2020, 10(2), 720; https://doi.org/10.3390/app10020720 - 20 Jan 2020
Abstract
The energy consumption used for domestic purposes in Europe is, to a considerable extent, due to heating and cooling. This energy is produced mostly by burning fossil fuels, which has a high negative environmental impact. The characteristics of a building are an important [...] Read more.
The energy consumption used for domestic purposes in Europe is, to a considerable extent, due to heating and cooling. This energy is produced mostly by burning fossil fuels, which has a high negative environmental impact. The characteristics of a building are an important factor to determine the necessities of heating and cooling loads. Therefore, the study of the relevant characteristics of the buildings, regarding the heating and cooling needed to maintain comfortable indoor air conditions, could be very useful in order to design and construct energy-efficient buildings. In previous studies, different machine-learning approaches have been used to predict heating and cooling loads from the set of variables: relative compactness, surface area, wall area, roof area, overall height, orientation, glazing area and glazing area distribution. However, none of these methods are based on fuzzy logic. In this research, we study two fuzzy logic approaches, i.e., fuzzy inductive reasoning (FIR) and adaptive neuro fuzzy inference system (ANFIS), to deal with the same problem. Fuzzy approaches obtain very good results, outperforming all the methods described in previous studies except one. In this work, we also study the feature selection process of FIR methodology as a pre-processing tool to select the more relevant variables before the use of any predictive modelling methodology. It is proven that FIR feature selection provides interesting insights into the main building variables causally related to heating and cooling loads. This allows better decision making and design strategies, since accurate cooling and heating load estimations and correct identification of parameters that affect building energy demands are of high importance to optimize building designs and equipment specifications. Full article
(This article belongs to the Special Issue Machine Learning for Energy Forecasting)
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Open AccessArticle
Prediction Interval Adjustment for Load-Forecasting using Machine Learning
Appl. Sci. 2019, 9(24), 5269; https://doi.org/10.3390/app9245269 - 04 Dec 2019
Abstract
Electricity load-forecasting is an essential tool for effective power grid operation and energy markets. However, the lack of accuracy on the estimation of the electricity demand may cause an excessive or insufficient supply which can produce instabilities in the power grid or cause [...] Read more.
Electricity load-forecasting is an essential tool for effective power grid operation and energy markets. However, the lack of accuracy on the estimation of the electricity demand may cause an excessive or insufficient supply which can produce instabilities in the power grid or cause load cuts. Hence, probabilistic load-forecasting methods have become more relevant since these allow an understanding of not only load-point forecasts but also the uncertainty associated with it. In this paper, we develop a probabilistic load-forecasting method based on Association Rules and Artificial Neural Networks for Short-Term Load Forecasting (2 h ahead). First, neural networks are used to estimate point-load forecasts and the variance between these and observations. Then, using the latter, a simple prediction interval is calculated. Next, association rules are employed to adjust the prediction intervals by exploiting the confidence and support of the association rules. The main idea is to increase certainty regarding predictions, thus reducing prediction interval width in accordance to the rules found. Results show that the presented methodology provides a closer prediction interval without sacrificing accuracy. Prediction interval quality and effectiveness is measured using Prediction Interval Coverage Probability (PICP) and the Dawid–Sebastiani Score (DSS). PICP and DSS per horizon shows that the Adjusted and Normal prediction intervals are similar. Also, probabilistic and point-forecast Means Absolute Error (MAE) and Root Mean Squared Error (RMSE) metrics are used. Probabilistic MAE indicates that Adjusted prediction intervals fail by less than 2.5 MW along the horizons, which is not significant if we compare it to the 1.3 MW of the Normal prediction interval failure. Also, probabilistic RMSE shows that the probabilistic error tends to be larger than MAE along the horizons, but the maximum difference between Adjusted and Normal probabilistic RMSE is less than 6 MW, which is also not significant. Full article
(This article belongs to the Special Issue Machine Learning for Energy Forecasting)
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Open AccessArticle
Probabilistic Forecasting of Short-Term Electric Load Demand: An Integration Scheme Based on Correlation Analysis and Improved Weighted Extreme Learning Machine
Appl. Sci. 2019, 9(20), 4215; https://doi.org/10.3390/app9204215 - 10 Oct 2019
Abstract
Precise prediction of short-term electric load demand is the key for developing power market strategies. Due to the dynamic environment of short-term load forecasting, probabilistic forecasting has become the center of attention for its ability of representing uncertainty. In this paper, an integration [...] Read more.
Precise prediction of short-term electric load demand is the key for developing power market strategies. Due to the dynamic environment of short-term load forecasting, probabilistic forecasting has become the center of attention for its ability of representing uncertainty. In this paper, an integration scheme mainly composed of correlation analysis and improved weighted extreme learning machine is proposed for probabilistic load forecasting. In this scheme, a novel cooperation of wavelet packet transform and correlation analysis is developed to deal with the data noise. Meanwhile, an improved weighted extreme learning machine with a new switch algorithm is provided to effectively obtain stable forecasting results. The probabilistic forecasting task is then accomplished by generating the confidence intervals with the Gaussian process. The proposed integration scheme, tested by actual data from Global Energy Forecasting Competition, is proved to have a better performance in graphic and numerical results than the other available methods. Full article
(This article belongs to the Special Issue Machine Learning for Energy Forecasting)
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