Utilizing Hybrid Feature Extraction, Tree-Based Learning and Data-Lightweight Model Re-Training for Accurate Short-Term and Day-Ahead Residential Load Forecasting

Residential load forecasting is one of the most important tasks of the overall supply management process in electrical grids, since it enables smart grid services such as demand response (DR). Hence, several approaches for accurate residential load forecasting have been proposed in the relevant literature. However, most of the existing methods focus on the forecasting performance and neglect other aspects of the problem like training time and model size (i.e. memory usage). In this paper, we introduce a new model for both short-term and day-ahead residential load forecasting. The model synthesizes an heterogeneous feature set, which is constituted by both automatically-selected lagged values from the load time series and manually-extracted temporal features. Then, the tree-based algorithm light gradient boosting machine (LGBM) is fed with the constructed feature set and used as a regression model. Finally, a data-lightweight strategy is used for retraining the proposed model, which leads to both high forecasting accuracy and low training times. The proposed model has been extensively evaluated on a large real-world residential load dataset. The experimental results indicate that the proposed model achieves both higher forecasting performance and lower training times and model sizes compared to state-of-the-art solutions.

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Utilizing Hybrid Feature Extraction, Tree-Based Learning and Data-Lightweight Model Re-Training for Accurate Short-Term and Day-Ahead Residential Load Forecasting

10.20944/preprints202108.0052.v1 ◽

2021 ◽

Author(s):

Christos S Ioakimidis ◽

Napoleon Bezas ◽

Christos Timplalexis ◽

Athanasios I. Salamanis ◽

Vasileios Karapatsias ◽

...

Keyword(s):

Relevant Literature ◽

Load Forecasting ◽

Gradient Boosting ◽

Short Term ◽

Training Time ◽

Light Gradient ◽

Electrical Grids ◽

Temporal Features ◽

Proposed Model ◽

Forecasting Performance

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Multistep-Ahead Solar Radiation Forecasting Scheme Based on the Light Gradient Boosting Machine: A Case Study of Jeju Island

Remote Sensing ◽

10.3390/rs12142271 ◽

2020 ◽

Vol 12 (14) ◽

pp. 2271 ◽

Cited By ~ 2

Author(s):

Jinwoong Park ◽

Jihoon Moon ◽

Seungmin Jung ◽

Eenjun Hwang

Keyword(s):

Solar Radiation ◽

Global Solar Radiation ◽

Jeju Island ◽

Gradient Boosting ◽

Probabilistic Forecasting ◽

Training Time ◽

Light Gradient ◽

Proposed Model ◽

Gradient Boosting Machine ◽

Time Problem

Smart islands have focused on renewable energy sources, such as solar and wind, to achieve energy self-sufficiency. Because solar photovoltaic (PV) power has the advantage of less noise and easier installation than wind power, it is more flexible in selecting a location for installation. A PV power system can be operated more efficiently by predicting the amount of global solar radiation for solar power generation. Thus far, most studies have addressed day-ahead probabilistic forecasting to predict global solar radiation. However, day-ahead probabilistic forecasting has limitations in responding quickly to sudden changes in the external environment. Although multistep-ahead (MSA) forecasting can be used for this purpose, traditional machine learning models are unsuitable because of the substantial training time. In this paper, we propose an accurate MSA global solar radiation forecasting model based on the light gradient boosting machine (LightGBM), which can handle the training-time problem and provide higher prediction performance compared to other boosting methods. To demonstrate the validity of the proposed model, we conducted a global solar radiation prediction for two regions on Jeju Island, the largest island in South Korea. The experiment results demonstrated that the proposed model can achieve better predictive performance than the tree-based ensemble and deep learning methods.

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Short-Term Energy Forecasting Using Machine-Learning-Based Ensemble Voting Regression

Symmetry ◽

10.3390/sym14010160 ◽

2022 ◽

Vol 14 (1) ◽

pp. 160

Author(s):

Pyae-Pyae Phyo ◽

Yung-Cheol Byun ◽

Namje Park

Keyword(s):

Machine Learning ◽

Mean Squared Error ◽

Weather Data ◽

Gradient Boosting ◽

Percentage Error ◽

Short Term ◽

Energy Forecasting ◽

Light Gradient ◽

Proposed Model ◽

Term Energy

Meeting the required amount of energy between supply and demand is indispensable for energy manufacturers. Accordingly, electric industries have paid attention to short-term energy forecasting to assist their management system. This paper firstly compares multiple machine learning (ML) regressors during the training process. Five best ML algorithms, such as extra trees regressor (ETR), random forest regressor (RFR), light gradient boosting machine (LGBM), gradient boosting regressor (GBR), and K neighbors regressor (KNN) are trained to build our proposed voting regressor (VR) model. Final predictions are performed using the proposed ensemble VR and compared with five selected ML benchmark models. Statistical autoregressive moving average (ARIMA) is also compared with the proposed model to reveal results. For the experiments, usage energy and weather data are gathered from four regions of Jeju Island. Error measurements, including mean absolute percentage error (MAPE), mean absolute error (MAE), and mean squared error (MSE) are computed to evaluate the forecasting performance. Our proposed model outperforms six baseline models in terms of the result comparison, giving a minimum MAPE of 0.845% on the whole test set. This improved performance shows that our approach is promising for symmetrical forecasting using time series energy data in the power system sector.

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A hybrid method using Exponential Smoothing and Gradient Boosting for electrical short-term load forecasting

2016 IEEE Latin American Conference on Computational Intelligence (LA-CCI) ◽

10.1109/la-cci.2016.7885697 ◽

2016 ◽

Cited By ~ 7

Author(s):

Victor Mayrink ◽

Henrique S. Hippert

Keyword(s):

Hybrid Method ◽

Load Forecasting ◽

Exponential Smoothing ◽

Gradient Boosting ◽

Short Term ◽

Short Term Load Forecasting

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Hybrid Empirical Mode Decomposition with Support Vector Regression Model for Short Term Load Forecasting

Energies ◽

10.3390/en12061093 ◽

2019 ◽

Vol 12 (6) ◽

pp. 1093 ◽

Cited By ~ 16

Author(s):

Wei-Chiang Hong ◽

Guo-Feng Fan

Keyword(s):

Support Vector Regression ◽

Load Forecasting ◽

Support Vector ◽

Electric Load ◽

Short Term ◽

Middle Term ◽

Mode Decomposition ◽

Random Term ◽

Forecasting Performance ◽

Short Term Load Forecasting

For operational management of power plants, it is desirable to possess more precise short-term load forecasting results to guarantee the power supply and load dispatch. The empirical mode decomposition (EMD) method and the particle swarm optimization (PSO) algorithm have been successfully hybridized with the support vector regression (SVR) to produce satisfactory forecasting performance in previous studies. Decomposed intrinsic mode functions (IMFs), could be further defined as three items: item A contains the random term and the middle term; item B contains the middle term and the trend (residual) term, and item C contains the middle terms only, where the random term represents the high-frequency part of the electric load data, the middle term represents the multiple-frequency part, and the trend term represents the low-frequency part. These three items would be modeled separately by the SVR-PSO model, and the final forecasting results could be calculated as A+B-C (the defined item D). Consequently, this paper proposes a novel electric load forecasting model, namely H-EMD-SVR-PSO model, by hybridizing these three defined items to improve the forecasting accuracy. Based on electric load data from the Australian electricity market, the experimental results demonstrate that the proposed H-EMD-SVR-PSO model receives more satisfied forecasting performance than other compared models.

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An Extreme Gradient Boosting Algorithm for Short-Term Load Forecasting Using Power Grid Big Data

Proceedings of 2018 Chinese Intelligent Systems Conference - Lecture Notes in Electrical Engineering ◽

10.1007/978-981-13-2288-4_46 ◽

2018 ◽

pp. 479-490

Author(s):

Liqiang Ren ◽

Limin Zhang ◽

Haipeng Wang ◽

Qiang Guo

Keyword(s):

Big Data ◽

Power Grid ◽

Load Forecasting ◽

Gradient Boosting ◽

Short Term ◽

Extreme Gradient Boosting ◽

Short Term Load Forecasting ◽

Boosting Algorithm

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Comparison of Short-Term Load Forecasting Performance by Neural Network and Autoregressive Based Models

2018 15th International Conference on the European Energy Market (EEM) ◽

10.1109/eem.2018.8469797 ◽

2018 ◽

Author(s):

M. Lopez ◽

S. Valero ◽

C. Sans ◽

C. Senabre ◽

A. Gabaldon

Keyword(s):

Neural Network ◽

Load Forecasting ◽

Short Term ◽

Forecasting Performance ◽

Short Term Load Forecasting

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Short-Term Load Forecasting Based on the Transformer Model

Information ◽

10.3390/info12120516 ◽

2021 ◽

Vol 12 (12) ◽

pp. 516

Author(s):

Zezheng Zhao ◽

Chunqiu Xia ◽

Lian Chi ◽

Xiaomin Chang ◽

Wei Li ◽

...

Keyword(s):

Load Forecasting ◽

Percentage Error ◽

Short Term ◽

Proposed Model ◽

Consumption Data ◽

Short Term Load Forecasting ◽

Distribution Process ◽

Set Up ◽

Transformer Model ◽

Similar Day

From the perspective of energy providers, accurate short-term load forecasting plays a significant role in the energy generation plan, efficient energy distribution process and electricity price strategy optimisation. However, it is hard to achieve a satisfactory result because the historical data is irregular, non-smooth, non-linear and noisy. To handle these challenges, in this work, we introduce a novel model based on the Transformer network to provide an accurate day-ahead load forecasting service. Our model contains a similar day selection approach involving the LightGBM and k-means algorithms. Compared to the traditional RNN-based model, our proposed model can avoid falling into the local minimum and outperforming the global search. To evaluate the performance of our proposed model, we set up a series of simulation experiments based on the energy consumption data in Australia. The performance of our model has an average MAPE (mean absolute percentage error) of 1.13, where RNN is 4.18, and LSTM is 1.93.

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Multi-Quantile Recurrent Neural Network for Feeder-Level Probabilistic Energy Disaggregation Considering Roof-Top Solar Energy

10.36227/techrxiv.16569735.v1 ◽

2021 ◽

Author(s):

Xiao-Yu Zhang ◽

Chris Watkins ◽

Stefanie Kuenzel

Keyword(s):

Neural Network ◽

Solar Energy ◽

Recurrent Neural Network ◽

Regression Tree ◽

Prediction Intervals ◽

Gradient Boosting ◽

Training Time ◽

Light Gradient ◽

Energy Disaggregation ◽

Net Load

The purpose of feeder-level energy disaggregation is to decouple the net load measured at the feeder-head into various components. This technology is vital for power system utilities since increased visibility of controllable loads enables the realization of demand-side management strategies. However, energy disaggregation at the feeder level is difficult to realize since the high-penetration of embedded generation masks the actual demand and different loads are highly aggregated. In this paper, the solar energy at the grid supply point is separated from the net load at first via either an unsupervised upscaling method or the supervised gradient boosting regression tree (GBRT) method. To deal with the uncertainty of the load components, the probabilistic energy disaggregation models based on multi-quantile recurrent neural network model (multi-quantile long short-term memory (MQ-LSTM) model and multi-quantile gated recurrent unit (MQ-GRU) model) are proposed to disaggregate the demand load into controlled loads (TCLs), non-thermostatically controlled loads (non-TCLs), and non-controllable loads. A variety of relevant information, including feeder measurements, meteorological measurements, calendar information, is adopted as the input features of the model. Instead of providing point prediction, the probabilistic model estimates the conditional quantiles and provides prediction intervals. A comprehensive case study is implemented to compare the proposed model with other state-of-the-art models (multi-quantile convolutional neural network (MQ-CNN), quantile gradient boosting regression tree (Q-GBRT), Quantile Light gradient boosting machine (Q-LGB)) from training time, reliability, sharpness, and overall performance aspects. The result shows that the MQ-LSTM can estimate reliable and sharp Prediction Intervals for target load components. And it shows the best performance among all algorithms with the shortest training time. Finally, a transfer learning algorithm is proposed to overcome the difficulty to obtain enough training data, and the model is pre-trained via synthetic data generated from a public database and then tested on the local dataset. The result confirms that the proposed energy disaggregation model is transferable and can be applied to other feeders easily. <br>

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A Two-Stage Short-Term Load Forecasting Method Using Long Short-Term Memory and Multilayer Perceptron

Energies ◽

10.3390/en14185873 ◽

2021 ◽

Vol 14 (18) ◽

pp. 5873

Author(s):

Yuhong Xie ◽

Yuzuru Ueda ◽

Masakazu Sugiyama

Keyword(s):

Neural Networks ◽

Multilayer Perceptron ◽

Short Term Memory ◽

Historical Data ◽

Load Forecasting ◽

Short Term ◽

Term Memory ◽

Proposed Model ◽

Short Term Load Forecasting ◽

Long Short Term Memory

Load forecasting is an essential task in the operation management of a power system. Electric power companies utilize short-term load forecasting (STLF) technology to make reasonable power generation plans. A forecasting model with low prediction errors helps reduce operating costs and risks for the operators. In recent years, machine learning has become one of the most popular technologies for load forecasting. In this paper, a two-stage STLF model based on long short-term memory (LSTM) and multilayer perceptron (MLP), which improves the forecasting accuracy over the entire time horizon, is proposed. In the first stage, a sequence-to-sequence (seq2seq) architecture, which can handle a multi-sequence of input to extract more features of historical data than that of single sequence, is used to make multistep predictions. In the second stage, the MLP is used for residual modification by perceiving other information that the LSTM cannot. To construct the model, we collected the electrical load, calendar, and meteorological records of Kanto region in Japan for four years. Unlike other LSTM-based hybrid architectures, the proposed model uses two independent neural networks instead of making the neural network deeper by concatenating a series of LSTM cells and convolutional neural networks (CNNs). Therefore, the proposed model is easy to be trained and more interpretable. The seq2seq module performs well in the first few hours of the predictions. The MLP inherits the advantage of the seq2seq module and improves the results by feeding artificially selected features both from historical data and information of the target day. Compared to the LSTM-AM model and single MLP model, the mean absolute percentage error (MAPE) of the proposed model decreases from 2.82% and 2.65% to 2%, respectively. The results demonstrate that the MLP helps improve the prediction accuracy of seq2seq module and the proposed model achieves better performance than other popular models. In addition, this paper also reveals the reason why the MLP achieves the improvement.

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