Multi-step ahead forecasting of heat load in district heating systems using machine learning algorithms

Xue, Puning; Jiao, Yi; Zhou, Zhigang; Chen, Xin; Xiu-mu, Fang; Liu, Jing

doi:10.1016/j.energy.2019.116085

Cited by 143 publications

(56 citation statements)

References 42 publications

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“…According to Ma and Fildes [66] , comparative studies between Recursive and Direct strategies have shown contradictory results as to which strategy achieves better forecasting performances than others. While Rana and Rahman [67] achieved better results for direct method over the recursive method, Xue et al [68] provided evidence of better results from the recursive method over the direct method. There is a trade-off between computational cost and accuracy in the choice of a multi-step-ahead forecasting method.…”

Section: Methodsmentioning

confidence: 99%

Multi-step ahead meningitis case forecasting based on decomposition and multi-objective optimization methods

Ribeiro

Mariani

Coelho

2020

Journal of Biomedical Informatics

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Epidemiological time series forecasting plays an important role in health public systems, due to its ability to allow managers to develop strategic planning to avoid possible epidemics. In this paper, a hybrid learning framework is developed to forecast multi-step-ahead (one, two, and three-month-ahead) meningitis cases in four states of Brazil. First, the proposed approach applies an ensemble empirical mode decomposition (EEMD) to decompose the data into intrinsic mode functions and residual components. Then, each component is used as the input of five different forecasting models, and, from there, forecasted results are obtained. Finally, all combinations of models and components are developed, and for each case, the forecasted results are weighted integrated (WI) to formulate a heterogeneous ensemble forecaster for the monthly meningitis cases. In the final stage, a multi-objective optimization (MOO) using the Non-Dominated Sorting Genetic Algorithm – version II is employed to find a set of candidates’ weights, and then the Technique for Order of Preference by similarity to Ideal Solution (TOPSIS) is applied to choose the adequate set of weights. Next, the most adequate model is the one with the best generalization capacity out-of-sample in terms of performance criteria including mean absolute error (MAE), relative root mean squared error (RRMSE), and symmetric mean absolute percentage error (sMAPE). By using MOO, the intention is to enhance the performance of the forecasting models by improving simultaneously their accuracy and stability measures. To access the model’s performance, comparisons based on metrics are conducted with: (i) EEMD, heterogeneous ensemble integrated by direct strategy, or simple sum; (ii) EEMD, homogeneous ensemble of components WI; (iii) models without signal decomposition. At this stage, MAE, RRMSE, and sMAPE criteria as well as Diebold–Mariano statistical test are adopted. In all twelve scenarios, the proposed framework was able to perform more accurate and stable forecasts, which showed, on 89.17% of the cases, that the errors of the proposed approach are statistically lower than other approaches. These results showed that combining EEMD, heterogeneous ensemble and WI with weights obtained by optimization can develop precise and stable forecasts. The modeling developed in this paper is promising and can be used by managers to support decision making.

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Section: Methodsmentioning

confidence: 99%

Multi-step ahead meningitis case forecasting based on decomposition and multi-objective optimization methods

Ribeiro

Mariani

Coelho

2020

Journal of Biomedical Informatics

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“…Because XGB provides the feature importance property, the authors of Reference [36] proposed a hybrid algorithm to classify similar days with K-means clustering fed by XGB feature importance results. Once the classification is done, an empirical mode method is used to decompose similar days' data into several intrinsic mode functions to train separated long short-term memory (LSTM) models, and finally, a time-series reconstruction from individual LSTM model predictions.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Their results achieved a 4.86% MAPE. Based on the results from References [10,36], ANN for STLF can outperform other forecasting methods if a robust hyperparameter optimization is performed to avoid the issues related to ANN tuning.…”

Section: Literature Reviewmentioning

confidence: 99%

Short-Term Electricity Load Forecasting with Machine Learning

Madrid

António

2021

Information

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An accurate short-term load forecasting (STLF) is one of the most critical inputs for power plant units’ planning commitment. STLF reduces the overall planning uncertainty added by the intermittent production of renewable sources; thus, it helps to minimize the hydrothermal electricity production costs in a power grid. Although there is some research in the field and even several research applications, there is a continual need to improve forecasts. This research proposes a set of machine learning (ML) models to improve the accuracy of 168 h forecasts. The developed models employ features from multiple sources, such as historical load, weather, and holidays. Of the five ML models developed and tested in various load profile contexts, the Extreme Gradient Boosting Regressor (XGBoost) algorithm showed the best results, surpassing previous historical weekly predictions based on neural networks. Additionally, because XGBoost models are based on an ensemble of decision trees, it facilitated the model’s interpretation, which provided a relevant additional result, the features’ importance in the forecasting.

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“…Each data set is split into two parts: the first 80% is employed to train the model, the rest 20% is utilized to validate. We compared the proposed TL-MCLSTM with the recursive XGBoost-based method because it obtained the best results in [22], MCSCNN-LSTM [6], CNN-LSTM [19], and a novel LSTM-based method [17]. MCSNN-LSTM and CNN-LSTM only need the raw power consumption sequence, and we utilized the authors' code to run, the code of CNN-LSTM is from http://www.keddiyan.com/files/PowerForecast.html.…”

Section: Comparative Analysismentioning

confidence: 99%

“…To overcome the shortcomings of direct strategy, the recursive strategy was proposed and applied. Xue et al [22] used one recursive strategy to multi-step heat loading forecasting based on the machine learning algorithm such as SVR, extreme gradient boosting (XGBoost), and deep neural network (DNN). The process of recursive strategy for multistep forecasting could be written as:…”

Section: Introductionmentioning

confidence: 99%

Multi-Step Short-Term Power Consumption Forecasting Using Multi-Channel LSTM With Time Location Considering Customer Behavior

Shao

Kim

2020

IEEE Access

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Short-Term Load Forecasting (STLF) is one critical assignment regarding the power supply and demand in the smart grid. Multi-step STLF provides strong evidence for decision-making to achieve consistent, quick supply and reduce direct or indirect cost. However, most of the current research only focuses on one-step STLF, which cannot satisfy the human-beings needs. Besides, short-term consumption fluctuates significantly in different periods and people, which increases the difficulty of forecasting. In this paper, we present a novel deep model named multi-channel long short-term memory (LSTM) with time location (TL-MCLSTM) in a multiple output strategy to forecast the multi-step short-term power consumption. The proposed model contains three channels: power consumption, time location, and customer behavior channels, respectively. Power consumption channel reflects the change and general trend of use; Time location channel reflects the hidden pattern of customer habits, which records the information consisting of time, day of the week, holidays. Moreover, we combine a convolution autoencoder and k-means to identify the type of behavior at the customer behavior channel. Power consumption and time location channels are trained individually through the LSTM as it has excellent memory function. Extracted features from LSTM in power consumption and time location channels are combined with customer behavior as comprehensive features to forecast. We designed, trained, and verified our proposed deep model on two nature data sets, and compared with other leading deep learning-based methods. The comparative studies have confirmed the effectiveness and priority of TL-MCLSTM for multi-step short-term consumption forecasting. INDEX TERMS Short-term Load Forecasting (STLF), smart grid, LSTM, multi-step forecasting, convolution autoencoder.

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Multi-step ahead forecasting of heat load in district heating systems using machine learning algorithms

Cited by 143 publications

References 42 publications

Multi-step ahead meningitis case forecasting based on decomposition and multi-objective optimization methods

Multi-step ahead meningitis case forecasting based on decomposition and multi-objective optimization methods

Short-Term Electricity Load Forecasting with Machine Learning

Multi-Step Short-Term Power Consumption Forecasting Using Multi-Channel LSTM With Time Location Considering Customer Behavior

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