Short-term electricity load forecasting using time series and ensemble learning methods

Short-term electricity load forecasting (STLF) plays a key role in operating the power system of a nation. A challenging problem in STLF is to deal with real-time data. This paper aims to address the problem using a hybrid online model. Online learning methods are becoming essential in STLF because load data often show complex seasonality (daily, weekly, annual) and changing patterns. Online models such as Online AutoRegressive Integrated Moving Average (Online ARIMA) and Online Recurrent neural network (Online RNN) can modify their parameters on the fly to adapt to the changes of real-time data. However, Online RNN alone cannot handle seasonality directly and ARIMA can only handle a single seasonal pattern (Seasonal ARIMA). In this study, we propose a hybrid online model that combines Online ARIMA, Online RNN, and Multi-seasonal decomposition to forecast real-time time series with multiple seasonal patterns. First, we decompose the original time series into three components: trend, seasonality, and residual. The seasonal patterns are modeled using Fourier series. This approach is flexible, allowing us to incorporate multiple periods. For trend and residual components, we employ Online ARIMA and Online RNN respectively to obtain the predictions. We use hourly load data of Vietnam and daily load data of Australia as case studies to verify our proposed model. The experimental results show that our model has better performance than single online models. The proposed model is robust and can be applied in many other fields with real-time time series.

Download Full-text

Short-term electricity load forecasting with Time Series Analysis

2017 IEEE International Conference on Prognostics and Health Management (ICPHM) ◽

10.1109/icphm.2017.7998331 ◽

2017 ◽

Cited By ~ 7

Author(s):

Hung Nguyen ◽

Christian K. Hansen

Keyword(s):

Time Series ◽

Time Series Analysis ◽

Load Forecasting ◽

Short Term ◽

Series Analysis ◽

Electricity Load ◽

Electricity Load Forecasting

Download Full-text

Comparison of Short-Term Electricity Load Forecasting Using Different Deep Learning Methods

European Journal of Science and Technology ◽

10.31590/ejosat.1017137 ◽

2021 ◽

Author(s):

İpek ATİK

Keyword(s):

Deep Learning ◽

Load Forecasting ◽

Short Term ◽

Learning Methods ◽

Electricity Load ◽

Electricity Load Forecasting

Download Full-text

Analysis of temperature-sensitive on short-term electricity load forecasting

2020 IEEE-HYDCON ◽

10.1109/hydcon48903.2020.9242910 ◽

2020 ◽

Author(s):

Nguyen Xuan Tung ◽

Nguyen Quang Dat ◽

Tran Ngoc Thang ◽

Vijender Kumar Solanki ◽

Nguyen Thi Ngoc Anh

Keyword(s):

Load Forecasting ◽

Temperature Sensitive ◽

Short Term ◽

Electricity Load ◽

Electricity Load Forecasting

Download Full-text

Empirical mode decomposition based denoising method with support vector regression for time series prediction: A case study for electricity load forecasting

Measurement ◽

10.1016/j.measurement.2017.02.007 ◽

2017 ◽

Vol 103 ◽

pp. 52-61 ◽

Cited By ~ 43

Author(s):

Yusuf Yaslan ◽

Bahadır Bican

Keyword(s):

Time Series ◽

Support Vector Regression ◽

Time Series Prediction ◽

Load Forecasting ◽

Support Vector ◽

Denoising Method ◽

Mode Decomposition ◽

Electricity Load ◽

Electricity Load Forecasting

Download Full-text

A Simple Method of Residential Electricity Load Forecasting by Improved Bayesian Neural Networks

Mathematical Problems in Engineering ◽

10.1155/2018/4276176 ◽

2018 ◽

Vol 2018 ◽

pp. 1-16 ◽

Cited By ~ 4

Author(s):

Shubin Zheng ◽

Qianwen Zhong ◽

Lele Peng ◽

Xiaodong Chai

Keyword(s):

Neural Networks ◽

Time Series ◽

Load Forecasting ◽

Forecast Model ◽

Time Range ◽

Simple Method ◽

Load Forecast ◽

Bayesian Neural Networks ◽

Electricity Load ◽

Electricity Load Forecasting

Electricity load forecasting is becoming one of the key issues to solve energy crisis problem, and time-series Bayesian Neural Network is one popular method used in load forecast models. However, it has long running time and relatively strong dependence on time and weather factors at a residential level. To solve these problems, this article presents an improved Bayesian Neural Networks (IBNN) forecast model by augmenting historical load data as inputs based on simple feedforward structure. From the load time delays correlations and impact factors analysis, containing different inputs, number of hidden neurons, historic period of data, forecasting time range, and range requirement of sample data, some advices are given on how to better choose these factors. To validate the performance of improved Bayesian Neural Networks model, several residential sample datasets of one whole year from Ausgrid have been selected to build the improved Bayesian Neural Networks model. The results compared with the time-series load forecast model show that the improved Bayesian Neural Networks model can significantly reduce calculating time by more than 30 times and even when the time or meteorological factors are missing, it can still predict the load with a high accuracy. Compared with other widely used prediction methods, the IBNN also performs a better accuracy and relatively shorter computing time. This improved Bayesian Neural Networks forecasting method can be applied in residential energy management.

Download Full-text

Deep-learning-based short-term electricity load forecasting: A real case application

Engineering Applications of Artificial Intelligence ◽

10.1016/j.engappai.2021.104645 ◽

2022 ◽

Vol 109 ◽

pp. 104645

Author(s):

Ibrahim Yazici ◽

Omer Faruk Beyca ◽

Dursun Delen

Keyword(s):

Deep Learning ◽

Load Forecasting ◽

Real Case ◽

Short Term ◽

Electricity Load ◽

Case Application ◽

Electricity Load Forecasting

Download Full-text

Electricity Load Forecasting Using Machine Learning Techniques

Business Intelligence in Economic Forecasting ◽

10.4018/978-1-61520-629-2.ch017 ◽

2010 ◽

pp. 318-336

Author(s):

Manuel Martín-Merino Acera

Keyword(s):

Machine Learning ◽

Time Series ◽

Load Forecasting ◽

Machine Learning Techniques ◽

Support Vector ◽

Strong Impact ◽

Local Character ◽

Nonlinear Character ◽

Electricity Load ◽

Electricity Load Forecasting

Electricity load forecasting has become increasingly important due to the strong impact on the operational efficiency of the power system. However, the accurate load prediction remains a challenging task due to several issues such as the nonlinear character of the time series or the seasonal patterns it exhibits. A large variety of techniques have been proposed to this aim, such as statistical models, fuzzy systems or artificial neural networks. The Support Vector Machines (SVM) have been widely applied to the electricity load forecasting with remarkable results. In this chapter, the authors study the performance of the classical SVM in the problem of electricity load forecasting. Next, an algorithm is developed that takes advantage of the local character of the time series. The method proposed first splits the time series into homogeneous regions using the Self Organizing Maps (SOM) and next trains a Support Vector Machine (SVM) locally in each region. The methods presented have been applied to the prediction of the maximum daily electricity demand. The properties of the time series are analyzed in depth. All the models are compared rigorously through several objective functions. The experimental results show that the local model proposed outperforms several statistical and machine learning forecasting techniques.

Download Full-text