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

Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 25145

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Prof. J. Carlos García-Díaz

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Department of Applied Statistics and Operational Research, and Quality, Universitat Politècnica de València, 46022 Valencia, Spain
Interests: time series analysis; short-term forecasting of electricity demand; forecasting in the time domain
Special Issues, Collections and Topics in MDPI journals

Dr. Óscar Trull

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Dear Colleagues,

Advanced societies are characterized by the intensive use of energy produced, distributed, and consumed in an uninterrupted, efficient, economic, reliable, and safe way. A case of special importance is electrical energy obtained as a result of a mix of fossil and renewable energy sources.

Predicting the power load is a crucial task for the proper functioning of the energy system within today’s liberalized energy markets. Improving the accuracy of prediction of energy demand as well as of peak loads to ensure the supply of energy by the energy system to end consumers has been of increasing interest to researchers in recent years.

The objective of this Special Issue is to present new, emerging methodologies that improve the traditional tools used in load forecasting. Artificial intelligence, machine learning, deep learning, and hybrid models are some of the new methods that can help improve decision-making in today’s energy markets, characterized by high uncertainty and volatility.

For this reason, we encourage researchers to submit their contributions in this field that represent advances in current scientific knowledge along with practical and/or real applications.

Prof. J.Carlos García-Díaz
Prof. Óscar Trull
Guest Editors

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Keywords

Short-term load forecasting
Statistical forecasting models (ARIMA, ARMAX, exponential smoothing, linear and non-linear regression)
Advanced forecasting methods
Artificial neural networks
Fuzzy regression models
Tree-based regression methods
Stacked and ensemble machine learning methods
Deep learning architectures
Support vector regression
Hybrid and ensemble machine learning
Adaptive load forecasting
Advanced optimization methods for energy demand forecast
Peak load forecasting

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Published Papers (6 papers)

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Research

16 pages, 13751 KiB

Open AccessArticle

Short-Term Load Forecasting Based on Deep Learning Bidirectional LSTM Neural Network

by Changchun Cai, Yuan Tao, Tianqi Zhu and Zhixiang Deng

Appl. Sci. 2021, 11(17), 8129; https://doi.org/10.3390/app11178129 - 1 Sep 2021

Cited by 28 | Viewed by 4164

Abstract

Accurate load forecasting guarantees the stable and economic operation of power systems. With the increasing integration of distributed generations and electrical vehicles, the variability and randomness characteristics of individual loads and the distributed generation has increased the complexity of power loads in power systems. Hence, accurate and robust load forecasting results are becoming increasingly important in modern power systems. The paper presents a multi-layer stacked bidirectional long short-term memory (LSTM)-based short-term load forecasting framework; the method includes neural network architecture, model training, and bootstrapping. In the proposed method, reverse computing is combined with forward computing, and a feedback calculation mechanism is designed to solve the coupling of before and after time-series information of the power load. In order to improve the convergence of the algorithm, deep learning training is introduced to mine the correlation between historical loads, and the multi-layer stacked style of the network is established to manage the power load information. Finally, actual data are applied to test the proposed method, and a comparison of the results of the proposed method with different methods shows that the proposed method can extract dynamic features from the data as well as make accurate predictions, and the availability of the proposed method is verified with real operational data. Full article

(This article belongs to the Special Issue Advanced Methods of Power Load Forecasting)

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15 pages, 1650 KiB

Open AccessArticle

Short-Term Load Forecasting Using an Attended Sequential Encoder-Stacked Decoder Model with Online Training

by Sylwia Henselmeyer and Marcin Grzegorzek

Appl. Sci. 2021, 11(11), 4927; https://doi.org/10.3390/app11114927 - 27 May 2021

Cited by 10 | Viewed by 1977

Abstract

The paper presents a new approach for the prediction of load active power 24 h ahead using an attended sequential encoder and stacked decoder model with Long Short-Term Memory cells. The load data are owned by the New York Independent System Operator (NYISO) and is dated from the years 2014–2017. Due to dynamics in the load patterns, multiple short pieces of training on pre-filtered data are executed in combination with the transfer learning concept. The evaluation is done by direct comparison with the results of the NYISO forecast and additionally under consideration of several benchmark methods. The results in terms of the Mean Absolute Percentage Error range from 1.5% for the highly loaded New York City zone to 3% for the Mohawk Valley zone with rather small load consumption. The execution time of a day ahead forecast including the training on a personal computer without GPU accounts to 10 s on average. Full article

(This article belongs to the Special Issue Advanced Methods of Power Load Forecasting)

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22 pages, 1130 KiB

Open AccessArticle

Short Term Electric Load Forecasting Based on Data Transformation and Statistical Machine Learning

by Nikos Andriopoulos, Aristeidis Magklaras, Alexios Birbas, Alex Papalexopoulos, Christos Valouxis, Sophia Daskalaki, Michael Birbas, Efthymios Housos and George P. Papaioannou

Appl. Sci. 2021, 11(1), 158; https://doi.org/10.3390/app11010158 - 26 Dec 2020

Cited by 38 | Viewed by 4852

Abstract

The continuous penetration of renewable energy resources (RES) into the energy mix and the transition of the traditional electric grid towards a more intelligent, flexible and interactive system, has brought electrical load forecasting to the foreground of smart grid planning and operation. Predicting the electric load is a challenging task due to its high volatility and uncertainty, either when it refers to the distribution system or to a single household. In this paper, a novel methodology is introduced which leverages the advantages of the state-of-the-art deep learning algorithms and specifically the Convolution Neural Nets (CNN). The main feature of the proposed methodology is the exploitation of the statistical properties of each time series dataset, so as to optimize the hyper-parameters of the neural network and in addition transform the given dataset into a form that allows maximum exploitation of the CNN algorithm’s advantages. The proposed algorithm is compared with the LSTM (Long Short Term Memory) technique which is the state of the art solution for electric load forecasting. The evaluation of the algorithms was conducted by employing three open-source, publicly available datasets. The experimental results show strong evidence of the effectiveness of the proposed methodology. Full article

(This article belongs to the Special Issue Advanced Methods of Power Load Forecasting)

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24 pages, 5634 KiB

Open AccessFeature PaperArticle

Forecasting Irregular Seasonal Power Consumption. An Application to a Hot-Dip Galvanizing Process

by Oscar Trull, Juan Carlos García-Díaz and Angel Peiró-Signes

Appl. Sci. 2021, 11(1), 75; https://doi.org/10.3390/app11010075 - 23 Dec 2020

Cited by 9 | Viewed by 2752

Abstract

Distribution companies use time series to predict electricity consumption. Forecasting techniques based on statistical models or artificial intelligence are used. Reliable forecasts are required for efficient grid management in terms of both supply and capacity. One common underlying feature of most demand–related time series is a strong seasonality component. However, in some cases, the electricity demanded by a process presents an irregular seasonal component, which prevents any type of forecast. In this article, we evaluated forecasting methods based on the use of multiple seasonal models: ARIMA, Holt-Winters models with discrete interval moving seasonality, and neural networks. The models are explained and applied to a real situation, for a node that feeds a galvanizing factory. The zinc hot-dip galvanizing process is widely used in the automotive sector for the protection of steel against corrosion. It requires enormous energy consumption, and this has a direct impact on companies’ income statements. In addition, it significantly affects energy distribution companies, as these companies must provide for instant consumption in their supply lines to ensure sufficient energy is distributed both for the process and for all the other consumers. The results show a substantial increase in the accuracy of predictions, which contributes to a better management of the electrical distribution. Full article

(This article belongs to the Special Issue Advanced Methods of Power Load Forecasting)

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19 pages, 6547 KiB

Open AccessArticle

Forecasting of Electrical Generation Using Prophet and Multiple Seasonality of Holt–Winters Models: A Case Study of Kuwait

by Abdulla I. Almazrouee, Abdullah M. Almeshal, Abdulrahman S. Almutairi, Mohammad R. Alenezi, Saleh N. Alhajeri and Faisal M. Alshammari

Appl. Sci. 2020, 10(23), 8412; https://doi.org/10.3390/app10238412 - 26 Nov 2020

Cited by 10 | Viewed by 2836

Abstract

Electrical generation forecasting is essential for management and policymakers due to the crucial data provided for resource planning. This research employs the Prophet model with single and multiple regressors to forecast the electricity generation in Kuwait from 2020 to 2030. In addition, multiple seasonality Holt–Winters models were utilized as a benchmark for comparative analysis. The accuracy, generalization, and robustness of the models were assessed based on different statistical performance metrics. The triple seasonality Holt–Winters model achieved superior performance compared with the other models with R² = 0.9899 and MAPE = 1.76%, followed by the double seasonality Holt–Winters model with R² = 0.9893 and MAPE = 1.83%. Moreover, the Prophet model with multiple regressors was the third-best performing model with R² = 0.9743 and MAPE = 2.77%. The forecasted annual generation in the year 2030 resulted in 92,535,555 kWh according to the best performing model. The study provides an outlook on the medium- and long-term electrical generation. Furthermore, the impact of fuel cost is investigated based on the five forecasting models to provide an insight for Kuwait’s policymakers. Full article

(This article belongs to the Special Issue Advanced Methods of Power Load Forecasting)

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17 pages, 5280 KiB

Open AccessArticle

Long-Term Forecasting of Electrical Loads in Kuwait Using Prophet and Holt–Winters Models

by Abdulla I. Almazrouee, Abdullah M. Almeshal, Abdulrahman S. Almutairi, Mohammad R. Alenezi and Saleh N. Alhajeri

Appl. Sci. 2020, 10(16), 5627; https://doi.org/10.3390/app10165627 - 13 Aug 2020

Cited by 63 | Viewed by 7195

Abstract

The rapidly increasing population growth and expansion of urban development are undoubtedly two of the main reasons for increasing global energy consumption. Accurate long-term forecasting of peak load is essential for saving time and money for countries’ power generation utilities. This paper introduces the first investigation into the performance of the Prophet model in the long-term peak load forecasting of Kuwait. The Prophet model is compared with the well-established Holt–Winters model to assess its feasibility and accuracy in forecasting long-term peak loads. Real data of electric load peaks from Kuwait powerplants from 2010 to 2020 were used for the electric load peaks, forecasting the peak load between 2020 and 2030. The Prophet model has shown more accurate predictions than the Holt–Winters model in five statistical performance metrics. Besides, the robustness of the two models was investigated by adding Gaussian white noise of different intensities. The Prophet model has proven to be more robust to noise than the Holt–Winters model. Furthermore, the generalizability test of the two models has shown that the Prophet model outperforms the Holt–Winters model. The reported results suggest that the forecasted maximum peak load is expected to reach 18,550 and 19,588 MW for the Prophet and Holt–Winters models by 2030 in Kuwait. The study suggests that the best months for scheduling the preventive maintenance for the year 2020 and 2021 are from November 2020 until March 2021 for both models. Full article

(This article belongs to the Special Issue Advanced Methods of Power Load Forecasting)

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