Combining Weather Stations for Electric Load Forecasting

Weather is a key factor affecting electricity demand. Many load forecasting models rely on weather variables. Weather stations provide point measurements of weather conditions in a service area. Since the load is spread geographically, a single weather station may not sufficiently explain the variations of the load over a vast area. Therefore, a proper combination of multiple weather stations plays a vital role in load forecasting. This paper answers the question: given a number of weather stations, how should they be combined for load forecasting? Simple averaging has been a commonly used and effective method in the literature. In this paper, we compared the performance of seven alternative methods with simple averaging as the benchmark using the data of the Global Energy Forecasting Competition 2012. The results demonstrate that some of the methods outperform the benchmark in combining weather stations. In addition, averaging the forecasts from these methods outperforms most individual methods.

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Electric Load Forecasting using Scatter Search Based Weighted Average Weather Conditions

The 2006 IEEE International Joint Conference on Neural Network Proceedings ◽

10.1109/ijcnn.2006.247393 ◽

2006 ◽

Cited By ~ 2

Author(s):

M. Kobayashi ◽

T. Yukawa ◽

Y. Kuze ◽

T. Matsui ◽

T. Iizaka ◽

...

Keyword(s):

Scatter Search ◽

Load Forecasting ◽

Weighted Average ◽

Weather Conditions ◽

Electric Load ◽

Electric Load Forecasting

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Electric Load Forecasting using Scatter Search Based Weighted Average Weather Conditions

The 2006 IEEE International Joint Conference on Neural Network Proceedings ◽

10.1109/ijcnn.2006.1716615 ◽

2006 ◽

Author(s):

M. Kobayashi ◽

T. Yukawa ◽

Y. Kuze ◽

T. Matsui ◽

T. Iizaka ◽

...

Keyword(s):

Scatter Search ◽

Load Forecasting ◽

Weighted Average ◽

Weather Conditions ◽

Electric Load ◽

Electric Load Forecasting

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Estimating Energy Forecasting Uncertainty for Reliable AI Autonomous Smart Grid Design

Energies ◽

10.3390/en14010247 ◽

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.

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Methods of weather variables introduction into short-term electric load forecasting models - a review

PRZEGLĄD ELEKTROTECHNICZNY ◽

10.15199/48.2017.04.18 ◽

2017 ◽

Vol 1 (4) ◽

pp. 72-75 ◽

Cited By ~ 1

Author(s):

Marcin JANICKI

Keyword(s):

Load Forecasting ◽

Electric Load ◽

Weather Variables ◽

Short Term ◽

Forecasting Models ◽

Electric Load Forecasting

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A Hybrid Model of EMD and PSO-SVR for Short-Term Load Forecasting in Residential Quarters

Mathematical Problems in Engineering ◽

10.1155/2016/9895639 ◽

2016 ◽

Vol 2016 ◽

pp. 1-10 ◽

Cited By ~ 9

Author(s):

Xiping Wang ◽

Yaqi Wang

Keyword(s):

Load Forecasting ◽

Vital Role ◽

Support Vector ◽

Electric Load ◽

Short Term ◽

Power Utility ◽

Mode Decomposition ◽

Short Term Load Forecasting ◽

Optimization Parameters ◽

Gain Optimization

Short-term load forecasting plays a vital role in the daily operational management of power utility. To improve the forecasting accuracy, this paper proposes a hybrid EMD-PSO-SVR forecasting model for short-term load forecasting based on empirical mode decomposition (EMD), support vector regression (SVR), and particle swarm optimization (PSO), also considering the effects of temperature, weekends, and holidays. EMD is used to decompose the residential electric load data into a number of intrinsic mode function (IMF) components and one residue; then SVR is constructed to forecast these IMFs and residual value individually. In order to gain optimization parameters of SVR, PSO is implemented to automatically perform the parameter selection in SVR modeling. Then all of these forecasting values are reconstructed to produce the final forecasting result for residential electric load data. Compared with the results from the EMD-SVR model, traditional SVR model, and PSO-SVR model, the result indicates that the proposed EMD-PSO-SVR model performs more effectively and more stably in forecasting the residential short-term load.

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