scholarly journals Short Term Load Forecasting Using TabNet: A Comparative Study with Traditional State-of-the-Art Regression Models

2021 ◽  
Vol 5 (1) ◽  
pp. 6
Author(s):  
Eugenio Borghini ◽  
Cinzia Giannetti
Keyword(s):  
Regression Models ◽  
Load Forecasting ◽  
Heat Pumps ◽  
Gradient Boosting ◽  
Electric Load ◽  
Short Term ◽  
Work Patterns ◽  
Short Term Load Forecasting ◽  
Machine Leaning ◽  
Traditional Approaches

Electric load forecasting is becoming increasingly challenging due to the growing penetration of decentralised energy generation and power-electronics based loads such as heat pumps and electric vehicles, which adds to a transition to more variable work patterns (accentuated by the COVID-19 pandemic in 2020). In this paper, three different Machine Leaning models are analysed to predict the energy load one week ahead for a period of time including the COVID-19 pandemic. It is shown that, by using the recently proposed TabNet model architecture, it is possible to achieve an accuracy comparable to more traditional approaches based on gradient boosting and artificial neural networks without the need of performing complex feature engineering.

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

Energies ◽  
2019 ◽  
Vol 12 (6) ◽  
pp. 1093 ◽  
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.

scholarly journals A Hybrid Short-Term Load Forecasting Framework with an Attention-Based Encoder–Decoder Network Based on Seasonal and Trend Adjustment

Energies ◽  
2019 ◽  
Vol 12 (24) ◽  
pp. 4612 ◽  
Author(s):  
Zhaorui Meng ◽  
Xianze Xu
Keyword(s):  
Power System ◽  
Load Forecasting ◽  
The United States ◽  
Attention Mechanism ◽  
Gradient Boosting ◽  
Percentage Error ◽  
Electrical Load ◽  
Short Term ◽  
Short Term Load Forecasting ◽  
Gated Recurrent Units

Accurate electrical load forecasting plays an important role in power system operation. An effective load forecasting approach can improve the operation efficiency of a power system. This paper proposes the seasonal and trend adjustment attention encoder–decoder (STA–AED), a hybrid short-term load forecasting approach based on a multi-head attention encoder–decoder module with seasonal and trend adjustment. A seasonal and trend decomposing technique is used to preprocess the original electrical load data. Each decomposed datum is regressed to predict the future electric load value by utilizing the encoder–decoder network with the multi-head attention mechanism. With the multi-head attention mechanism, STA–AED can interpret the prediction results more effectively. A large number of experiments and extensive comparisons have been carried out with a load forecasting dataset from the United States. The proposed hybrid STA–AED model is superior to the other five counterpart models such as random forest, gradient boosting decision tree (GBDT), gated recurrent units (GRUs), Encoder–Decoder, and Encoder–Decoder with multi-head attention. The proposed hybrid model shows the best prediction accuracy in 14 out of 15 zones in terms of both root mean square error (RMSE) and mean absolute percentage error (MAPE).

Short-Term Load Forecasting With Different Aggregation Strategies

2018 ◽  
Author(s):  
Cong Feng ◽  
Jie Zhang
Keyword(s):  
Load Forecasting ◽  
Information Aggregation ◽  
Machine Learning Algorithms ◽  
Weather Data ◽  
Gradient Boosting ◽  
Support Vector ◽  
Short Term ◽  
Calendar Effects ◽  
Network Support ◽  
Short Term Load Forecasting

Effective short-term load forecasting (STLF) plays an important role in demand-side management and power system operations. In this paper, STLF with three aggregation strategies are developed, which are information aggregation (IA), model aggregation (MA), and hierarchy aggregation (HA). The IA, MA, and HA strategies aggregate inputs, models, and forecasts, respectively, at different stages in the forecasting process. To verify the effectiveness of the three aggregation STLF, a set of 10 models based on 4 machine learning algorithms, i.e., artificial neural network, support vector machine, gradient boosting machine, and random forest, are developed in each aggregation group to predict 1-hour-ahead load. Case studies based on 2-year of university campus data with 13 individual buildings showed that: (a) STLF with three aggregation strategies improves forecasting accuracy, compared with benchmarks without aggregation; (b) STLF-IA consistently presents superior behavior than STLF based on weather data and STLF based on individual load data; (c) MA reduces the occurrence of unsatisfactory single-algorithm STLF models, therefore enhancing the STLF robustness; (d) STLF-HA produces the most accurate forecasts in distinctive load pattern scenarios due to calendar effects.

scholarly journals Estimating Energy Forecasting Uncertainty for Reliable AI Autonomous Smart Grid Design

Energies ◽  
2021 ◽  
Vol 14 (1) ◽  
pp. 247
Author(s):  
Maher Selim ◽  
Ryan Zhou ◽  
Wenying Feng ◽  
Peter Quinsey
Keyword(s):  
Deep Learning ◽  
Smart Grid ◽  
Smart Grids ◽  
Short Term Memory ◽  
Load Forecasting ◽  
Gradient Boosting ◽  
Electric Load ◽  
Short Term ◽  
Energy Forecasting ◽  
Uncertainty Estimates

Building safe, reliable, fully automated energy smart grid systems requires a trustworthy electric load forecasting system. Recent work has shown the efficacy of Long Short-Term Memory neural networks in energy load forecasting. However, such predictions do not come with an estimate of uncertainty, which can be dangerous when critical decisions are being made autonomously in energy production and distribution. In this paper, we present methods for evaluating the uncertainty in short-term electrical load predictions for both deep learning and gradient tree boosting. We train Bayesian deep learning and gradient boosting models with real electric load data and show that an uncertainty estimate may be obtained alongside the prediction itself with minimal loss of accuracy. We find that the uncertainty estimates obtained are robust to changes in the input features. This result is an important step in building reliable autonomous smart grids.

scholarly journals Short-Term Load Forecasting using PSO Based Local Linear Wavelet Neural Network

2011 ◽  
pp. 163-168
Author(s):  
Prasanta Kumar Pany
Keyword(s):  
Load Forecasting ◽  
Operational Planning ◽  
Energy Management System ◽  
Electric Load ◽  
Wavelet Neural Networks ◽  
Short Term ◽  
Local Linear ◽  
Short Term Load Forecasting ◽  
Electric Loads ◽  
Method Show

Short-term load forecasting (STLF) plays an important role in the operational planning security functions of an energy management system. The short term load forecasting is aimed at predicting electric loads for a period of minutes, hours, days or week for the purpose of providing fundamental load profiles to the system. The work presented in this paper makes use of PSO based local linear wavelet neural networks (LLWNN) to find the electric load for a given period, with a certain confidence level. The results of the new method show significant improvement in the load forecasting process.

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