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Data Science and Big Data in Energy Forecasting

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A special issue of Energies (ISSN 1996-1073).

Deadline for manuscript submissions: closed (5 February 2018) | Viewed by 98615

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Special Issue Editors

Dr. José C. Riquelme

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Guest Editor

Department of Languages and Computer Systems, University of Seville, 41012 Sevilla, Spain
Interests: machine learning; data mining; big data; smart grids
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Alicia Troncoso

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Guest Editor

Data Science & Big Data Lab, Pablo de Olavide University, ES-41013 Seville, Spain
Interests: time series; forecasting; data science and big data
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Francisco Martínez-Álvarez

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Guest Editor

Department of Computer Science, Pablo de Olavide University, ES-41013 Seville, Spain
Interests: big data; machine learning; time series; forecasting
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue focuses on the forecasting of time series, with particular emphasis on energy-related data by means of data science and big data techniques. By energy, we understand any kind of energy, such as electrical, solar, microwave, wind, etc.

Very powerful approaches have been developed in the context of data science and big data analytics during the last years. Such approaches deal with large datasets, considering all samples and measurements, as well as including many additional features. With them, automated machine learning methods for extracting relevant patterns, high performance computing or data visualization are being successfully applied to energy time series forecasting nowadays.

For all the aforementioned, we encourage researchers to share their original works in the field of energy time series forecasting. Topics of primary interest include, but are not limited to:

(1)   Data science and big data in energy time series analysis.
(2)   Data science and big data in energy time series modelling.
(3)   Data science and big data in energy-related time series forecasting.
(4)   Data science and big data in non-parametric time series approaches.

Prof. Dr. José C. Riquelme
Prof. Dr. Alicia Troncoso
Prof. Dr. Francisco Martínez-Álvarez
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

energy
time series
forecasting
data science
big data

Published Papers (14 papers)

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Editorial

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2 pages, 147 KiB

Open AccessEditorial

Data Science and Big Data in Energy Forecasting

by Francisco Martínez-Álvarez, Alicia Troncoso and José C. Riquelme

Energies 2018, 11(11), 3224; https://doi.org/10.3390/en11113224 - 21 Nov 2018

Cited by 3 | Viewed by 2958

Abstract

This editorial summarizes the performance of the special issue entitled Data Science and Big Data in Energy Forecasting, which was published at MDPI’s Energies journal. The special issue took place in 2017 and accepted a total of 13 papers from 7 different countries. Electrical, solar and wind energy forecasting were the most analyzed topics, introducing new methods with applications of utmost relevance. Full article

(This article belongs to the Special Issue Data Science and Big Data in Energy Forecasting)

Research

Jump to: Editorial

24 pages, 1831 KiB

Open AccessArticle

Short-Term Electricity Demand Forecasting Using a Functional State Space Model

by Komi Nagbe, Jairo Cugliari and Julien Jacques

Energies 2018, 11(5), 1120; https://doi.org/10.3390/en11051120 - 02 May 2018

Cited by 34 | Viewed by 3817

Abstract

In the past several years, the liberalization of the electricity supply, the increase in variability of electric appliances and their use, and the need to respond to the electricity demand in real time has made electricity demand forecasting a challenge. To this challenge, many solutions are being proposed. The electricity demand involves many sources such as economic activities, household need and weather sources. All of these sources make electricity demand forecasting difficult. To forecast the electricity demand, some proposed parametric methods that integrate main variables that are sources of electricity demand. Others proposed a non parametric method such as pattern recognition methods. In this paper, we propose to take only the past electricity consumption information embedded in a functional vector autoregressive state space model to forecast the future electricity demand. The model we proposed aims to be applied at some aggregation level between regional and nation-wide grids. To estimate the parameters of this model, we use likelihood maximization, spline smoothing, functional principal components analysis and Kalman filtering. Through numerical experiments on real datasets, both from supplier Enercoop and from the Transmission System Operator of the French nation-wide grid, we show the appropriateness of the approach. Full article

(This article belongs to the Special Issue Data Science and Big Data in Energy Forecasting)

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31 pages, 899 KiB

Open AccessArticle

Stacking Ensemble Learning for Short-Term Electricity Consumption Forecasting

by Federico Divina, Aude Gilson, Francisco Goméz-Vela, Miguel García Torres and José F. Torres

Energies 2018, 11(4), 949; https://doi.org/10.3390/en11040949 - 16 Apr 2018

Cited by 153 | Viewed by 10524

Abstract

The ability to predict short-term electric energy demand would provide several benefits, both at the economic and environmental level. For example, it would allow for an efficient use of resources in order to face the actual demand, reducing the costs associated to the [...] Read more.

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. To this aim, in this paper we propose a strategy based on ensemble learning in order to tackle the short-term load forecasting problem. In particular, our approach is based on a stacking ensemble learning scheme, where the predictions produced by three base learning methods are used by a top level method in order to produce final predictions. We tested the proposed scheme on a dataset reporting the energy consumption in Spain over more than nine years. The obtained experimental results show that an approach for short-term electricity consumption forecasting based on ensemble learning can help in combining predictions produced by weaker learning methods in order to obtain superior results. In particular, the system produces a lower error with respect to the existing state-of-the art techniques used on the same dataset. More importantly, this case study has shown that using an ensemble scheme can achieve very accurate predictions, and thus that it is a suitable approach for addressing the short-term load forecasting problem. Full article

(This article belongs to the Special Issue Data Science and Big Data in Energy Forecasting)

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21 pages, 1567 KiB

Open AccessFeature PaperArticle

Identifying Health Status of Wind Turbines by Using Self Organizing Maps and Interpretation-Oriented Post-Processing Tools

by Alejandro Blanco-M., Karina Gibert, Pere Marti-Puig, Jordi Cusidó and Jordi Solé-Casals

Energies 2018, 11(4), 723; https://doi.org/10.3390/en11040723 - 22 Mar 2018

Cited by 22 | Viewed by 4437

Abstract

Background: Identifying the health status of wind turbines becomes critical to reduce the impact of failures on generation costs (between 25–35%). This is a time-consuming task since a human expert has to explore turbines individually. Methods: To optimize this process, we present a strategy based on Self Organizing Maps, clustering and a further grouping of turbines based on the centroids of their SOM clusters, generating groups of turbines that have similar behavior for subsystem failure. The human expert can diagnose the wind farm health by the analysis of a small each group sample. By introducing post-processing tools like Class panel graphs and Traffic lights panels, the conceptualization of the clusters is enhanced, providing additional information of what kind of real scenarios the clusters point out contributing to a better diagnosis. Results: The proposed approach has been tested in real wind farms with different characteristics (number of wind turbines, manufacturers, power, type of sensors, ...) and compared with classical clustering. Conclusions: Experimental results show that the states healthy, unhealthy and intermediate have been detected. Besides, the operational modes identified for each wind turbine overcome those obtained with classical clustering techniques capturing the intrinsic stationarity of the data. Full article

(This article belongs to the Special Issue Data Science and Big Data in Energy Forecasting)

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19 pages, 3094 KiB

Open AccessEditor’s ChoiceArticle

Big Data Analytics for Discovering Electricity Consumption Patterns in Smart Cities

by Rubén Pérez-Chacón, José M. Luna-Romera, Alicia Troncoso, Francisco Martínez-Álvarez and José C. Riquelme

Energies 2018, 11(3), 683; https://doi.org/10.3390/en11030683 - 18 Mar 2018

Cited by 77 | Viewed by 9877

Abstract

New technologies such as sensor networks have been incorporated into the management of buildings for organizations and cities. Sensor networks have led to an exponential increase in the volume of data available in recent years, which can be used to extract consumption patterns for the purposes of energy and monetary savings. For this reason, new approaches and strategies are needed to analyze information in big data environments. This paper proposes a methodology to extract electric energy consumption patterns in big data time series, so that very valuable conclusions can be made for managers and governments. The methodology is based on the study of four clustering validity indices in their parallelized versions along with the application of a clustering technique. In particular, this work uses a voting system to choose an optimal number of clusters from the results of the indices, as well as the application of the distributed version of the k-means algorithm included in Apache Spark’s Machine Learning Library. The results, using electricity consumption for the years 2011–2017 for eight buildings of a public university, are presented and discussed. In addition, the performance of the proposed methodology is evaluated using synthetic big data, which cab represent thousands of buildings in a smart city. Finally, policies derived from the patterns discovered are proposed to optimize energy usage across the university campus. Full article

(This article belongs to the Special Issue Data Science and Big Data in Energy Forecasting)

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24 pages, 45826 KiB

Open AccessArticle

A Reduced Order Model to Predict Transient Flows around Straight Bladed Vertical Axis Wind Turbines

by Soledad Le Clainche and Esteban Ferrer

Energies 2018, 11(3), 566; https://doi.org/10.3390/en11030566 - 07 Mar 2018

Cited by 37 | Viewed by 4155

Abstract

We develop a reduced order model to represent the complex flow behaviour around vertical axis wind turbines. First, we simulate vertical axis turbines using an accurate high order discontinuous Galerkin–Fourier Navier–Stokes Large Eddy Simulation solver with sliding meshes and extract flow snapshots in time. Subsequently, we construct a reduced order model based on a high order dynamic mode decomposition approach that selects modes based on flow frequency. We show that only a few modes are necessary to reconstruct the flow behaviour of the original simulation, even for blades rotating in turbulent regimes. Furthermore, we prove that an accurate reduced order model can be constructed using snapshots that do not sample one entire turbine rotation (but only a fraction of it), which reduces the cost of generating the reduced order model. Additionally, we compare the reduced order model based on the high order Navier–Stokes solver to fast 2D simulations (using a Reynolds Averaged Navier–Stokes turbulent model) to illustrate the good performance of the proposed methodology. Full article

(This article belongs to the Special Issue Data Science and Big Data in Energy Forecasting)

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15 pages, 21969 KiB

Open AccessEditor’s ChoiceArticle

Wind Predictions Upstream Wind Turbines from a LiDAR Database

by Soledad Le Clainche, Luis S. Lorente and José M. Vega

Energies 2018, 11(3), 543; https://doi.org/10.3390/en11030543 - 03 Mar 2018

Cited by 31 | Viewed by 4015

Abstract

This article presents a new method to predict the wind velocity upstream a horizontal axis wind turbine from a set of light detection and ranging (LiDAR) measurements. The method uses higher order dynamic mode decomposition (HODMD) to construct a reduced order model (ROM) that can be extrapolated in space. LiDAR measurements have been carried out upstream a wind turbine at six different planes perpendicular to the wind turbine axis. This new HODMD-based ROM predicts with high accuracy the wind velocity during a timespan of 24 h in a plane of measurements that is more than 225 m far away from the wind turbine. Moreover, the technique introduced is general and obtained with an almost negligible computational cost. This fact makes it possible to extend its application to both vertical axis wind turbines and real-time operation. Full article

(This article belongs to the Special Issue Data Science and Big Data in Energy Forecasting)

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22 pages, 1014 KiB

Open AccessArticle

Wind Power Forecasting Based on Echo State Networks and Long Short-Term Memory

by Erick López, Carlos Valle, Héctor Allende, Esteban Gil and Henrik Madsen

Energies 2018, 11(3), 526; https://doi.org/10.3390/en11030526 - 28 Feb 2018

Cited by 85 | Viewed by 6646

Abstract

Wind power generation has presented an important development around the world. However, its integration into electrical systems presents numerous challenges due to the variable nature of the wind. Therefore, to maintain an economical and reliable electricity supply, it is necessary to accurately predict wind generation. The Wind Power Prediction Tool (WPPT) has been proposed to solve this task using the power curve associated with a wind farm. Recurrent Neural Networks (RNNs) model complex non-linear relationships without requiring explicit mathematical expressions that relate the variables involved. In particular, two types of RNN, Long Short-Term Memory (LSTM) and Echo State Network (ESN), have shown good results in time series forecasting. In this work, we present an LSTM+ESN architecture that combines the characteristics of both networks. An architecture similar to an ESN is proposed, but using LSTM blocks as units in the hidden layer. The training process of this network has two key stages: (i) the hidden layer is trained with a descending gradient method online using one epoch; (ii) the output layer is adjusted with a regularized regression. In particular, the case is proposed where Step (i) is used as a target for the input signal, in order to extract characteristics automatically as the autoencoder approach; and in the second stage (ii), a quantile regression is used in order to obtain a robust estimate of the expected target. The experimental results show that LSTM+ESN using the autoencoder and quantile regression outperforms the WPPT model in all global metrics used. Full article

(This article belongs to the Special Issue Data Science and Big Data in Energy Forecasting)

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17 pages, 4030 KiB

Open AccessArticle

Do Customers Choose Proper Tariff? Empirical Analysis Based on Polish Data Using Unsupervised Techniques

by Rafik Nafkha, Krzysztof Gajowniczek and Tomasz Ząbkowski

Energies 2018, 11(3), 514; https://doi.org/10.3390/en11030514 - 27 Feb 2018

Cited by 15 | Viewed by 3037

Abstract

Individual electricity customers that are connected to low voltage network in Poland are usually assigned to the most common G11 tariff group with flat prices for the whole year, no matter the usage volume. Given the diversity of customers’ behavior inside the same specific group, we aim to propose an approach to assign the customers based on some objective factors rather than subjective fixed assignment. With the smart metering data and statistical methods for clustering we can explore and recommend each customer the most suitable tariff to benefit from lower prices thus generate the savings. Further, the paper applies hierarchical, k-means and Kohonen approaches to assign the customers to the proper tariff, assuming that the customer can gain the biggest expenses reduction from the tariff switch. The analysis was conducted based on the Polish dataset with an hourly energy readings among 197 entities. Full article

(This article belongs to the Special Issue Data Science and Big Data in Energy Forecasting)

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26 pages, 7908 KiB

Open AccessArticle

Big Data Mining of Energy Time Series for Behavioral Analytics and Energy Consumption Forecasting

by Shailendra Singh and Abdulsalam Yassine

Energies 2018, 11(2), 452; https://doi.org/10.3390/en11020452 - 20 Feb 2018

Cited by 169 | Viewed by 14735

Abstract

Responsible, efficient and environmentally aware energy consumption behavior is becoming a necessity for the reliable modern electricity grid. In this paper, we present an intelligent data mining model to analyze, forecast and visualize energy time series to uncover various temporal energy consumption patterns. These patterns define the appliance usage in terms of association with time such as hour of the day, period of the day, weekday, week, month and season of the year as well as appliance-appliance associations in a household, which are key factors to infer and analyze the impact of consumers’ energy consumption behavior and energy forecasting trend. This is challenging since it is not trivial to determine the multiple relationships among different appliances usage from concurrent streams of data. Also, it is difficult to derive accurate relationships between interval-based events where multiple appliance usages persist for some duration. To overcome these challenges, we propose unsupervised data clustering and frequent pattern mining analysis on energy time series, and Bayesian network prediction for energy usage forecasting. We perform extensive experiments using real-world context-rich smart meter datasets. The accuracy results of identifying appliance usage patterns using the proposed model outperformed Support Vector Machine (SVM) and Multi-Layer Perceptron (MLP) at each stage while attaining a combined accuracy of 81.82%, 85.90%, 89.58% for 25%, 50% and 75% of the training data size respectively. Moreover, we achieved energy consumption forecast accuracies of 81.89% for short-term (hourly) and 75.88%, 79.23%, 74.74%, and 72.81% for the long-term; i.e., day, week, month, and season respectively. Full article

(This article belongs to the Special Issue Data Science and Big Data in Energy Forecasting)

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6785 KiB

Open AccessArticle

Nearshore Wave Predictions Using Data Mining Techniques during Typhoons: A Case Study near Taiwan’s Northeastern Coast

by Chih-Chiang Wei

Energies 2018, 11(1), 11; https://doi.org/10.3390/en11010011 - 21 Dec 2017

Cited by 20 | Viewed by 3976

Abstract

Seasonal typhoons provide energy into the wave field in summer and autumn in Taiwan. Typhoons lead to abundant wave energy near the coastal area and cause storm surges that can destroy offshore facilities. The potential for wave energy can be obtained from analyzing the wave height. To develop an effective model for predicting typhoon-induced wave height near coastal areas, this study employed various popular data mining models—namely k-nearest neighbors (kNN), linear regressions (LR), model trees (M5), multilayer perceptron (MLP) neural network, and support vector regression (SVR) algorithms—as forecasting techniques. The principal component analysis (PCA) was then performed to reduce the potential variables from the original data at the first stage of data preprocessing. The experimental site was the Longdong buoy off the northeastern coast of Taiwan. Data on typhoons that occurred during 2002–2011 and 2012–2013 were collected for training and testing, respectively. This study designed four PCA cases, namely EV1, TV90, TV95, and ORI: EV1 used eigenvalues higher than 1.0 as principal components; TV90 and TV95 used the total variance percentages of 90% and 95%, respectively; and ORI used the original data. The forecast horizons varying from 1 h to 6 h were evaluated. The results show that (1) in the PCA model’ cases, when the number of attributes decreases, computing time decreases and prediction error increases; (2) regarding classified wave heights, M5 provides excellent outcomes at the small wavelet wavelet level; MLP has favorable outcomes at the large wavelet and small/moderate wave levels; meanwhile, SVR gives optimal outcomes at the long wave and high/very high wave levels; and (3) for performance of lead times, MLP and SVR achieve more favorable relative weighted performance without consideration of computational complexity; however, MLP and SVR might obtain lower performance when computational complexity is considered. Full article

(This article belongs to the Special Issue Data Science and Big Data in Energy Forecasting)

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23206 KiB

Open AccessEditor’s ChoiceArticle

Predictions of Surface Solar Radiation on Tilted Solar Panels using Machine Learning Models: A Case Study of Tainan City, Taiwan

by Chih-Chiang Wei

Energies 2017, 10(10), 1660; https://doi.org/10.3390/en10101660 - 20 Oct 2017

Cited by 30 | Viewed by 7944

Abstract

In this paper, forecasting models were constructed to estimate surface solar radiation on an hourly basis and the solar irradiance received by solar panels at different tilt angles, to enhance the capability of photovoltaic systems by estimating the amount of electricity they generate, thereby improving the reliability of the power they supply. The study site was Tainan in southern Taiwan, which receives abundant sunlight because of its location at a latitude of approximately 23°. Four forecasting models of surface solar irradiance were constructed, using the multilayer perceptron (MLP), random forests (RF), k-nearest neighbors (kNN), and linear regression (LR), algorithms, respectively. The forecast horizon ranged from 1 to 12 h. The findings are as follows: first, solar irradiance was effectively estimated when a combination of ground weather data and solar position data was applied. Second, the mean absolute error was higher in MLP than in RF and kNN, and LR had the worst predictive performance. Third, the observed total solar irradiance was 1.562 million w/m² per year when the solar-panel tilt angle was 0° (i.e., the non-tilted position) and peaked at 1.655 million w/m² per year when the angle was 20–22°. The level of the irradiance was almost the same when the solar-panel tilt angle was 0° as when the angle was 41°. In summary, the optimal solar-panel tilt angle in Tainan was 20–22°. Full article

(This article belongs to the Special Issue Data Science and Big Data in Energy Forecasting)

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1918 KiB

Open AccessEditor’s ChoiceArticle

Building Energy Consumption Prediction: An Extreme Deep Learning Approach

by Chengdong Li, Zixiang Ding, Dongbin Zhao, Jianqiang Yi and Guiqing Zhang

Energies 2017, 10(10), 1525; https://doi.org/10.3390/en10101525 - 07 Oct 2017

Cited by 214 | Viewed by 15573

Abstract

Building energy consumption prediction plays an important role in improving the energy utilization rate through helping building managers to make better decisions. However, as a result of randomness and noisy disturbance, it is not an easy task to realize accurate prediction of the building energy consumption. In order to obtain better building energy consumption prediction accuracy, an extreme deep learning approach is presented in this paper. The proposed approach combines stacked autoencoders (SAEs) with the extreme learning machine (ELM) to take advantage of their respective characteristics. In this proposed approach, the SAE is used to extract the building energy consumption features, while the ELM is utilized as a predictor to obtain accurate prediction results. To determine the input variables of the extreme deep learning model, the partial autocorrelation analysis method is adopted. Additionally, in order to examine the performances of the proposed approach, it is compared with some popular machine learning methods, such as the backward propagation neural network (BPNN), support vector regression (SVR), the generalized radial basis function neural network (GRBFNN) and multiple linear regression (MLR). Experimental results demonstrate that the proposed method has the best prediction performance in different cases of the building energy consumption. Full article

(This article belongs to the Special Issue Data Science and Big Data in Energy Forecasting)

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2633 KiB

Open AccessArticle

A Hybrid Wind Speed Forecasting System Based on a ‘Decomposition and Ensemble’ Strategy and Fuzzy Time Series

by Hufang Yang, Zaiping Jiang and Haiyan Lu

Energies 2017, 10(9), 1422; https://doi.org/10.3390/en10091422 - 16 Sep 2017

Cited by 43 | Viewed by 4873

Abstract

Accurate and stable wind speed forecasting is of critical importance in the wind power industry and has measurable influence on power-system management and the stability of market economics. However, most traditional wind speed forecasting models require a large amount of historical data and face restrictions due to assumptions, such as normality postulates. Additionally, any data volatility leads to increased forecasting instability. Therefore, in this paper, a hybrid forecasting system, which combines the ‘decomposition and ensemble’ strategy and fuzzy time series forecasting algorithm, is proposed that comprises two modules—data pre-processing and forecasting. Moreover, the statistical model, artificial neural network, and Support Vector Regression model are employed to compare with the proposed hybrid system, which is proven to be very effective in forecasting wind speed data affected by noise and instability. The results of these comparisons demonstrate that the hybrid forecasting system can improve the forecasting accuracy and stability significantly, and supervised discretization methods outperform the unsupervised methods for fuzzy time series in most cases. Full article

(This article belongs to the Special Issue Data Science and Big Data in Energy Forecasting)

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