scholarly journals Online SARIMA applied for short-term electricity load forecasting

2021 ◽  
Author(s):  
Quang Dat Nguyen ◽  
Nhat Anh Nguyen ◽  
Ngoc Thang Tran ◽  
Vijender Kumar Solanki ◽  
Rubén González Crespo ◽  
...  
Keyword(s):  
Power Systems ◽  
Moving Average ◽  
Supply And Demand ◽  
Load Forecasting ◽  
Percentage Error ◽  
Short Term ◽  
Autoregressive Integrated Moving Average ◽  
Proposed Model ◽  
Short Term Load Forecasting ◽  
Electricity Load

Abstract Short-term Load Forecasting (STLF) plays a crucial role in balancing supply and demand of load dispatching operation, ensures stability for the power system. With the advancement of real-time smart sensors in power systems, it is of great significance to develop techniques to handle data streams on-the-fly to improve operational efficiency. In this paper, we propose an online variant of Seasonal Autoregressive Integrated Moving Average (SARIMA) to forecast electricity load sequentially. The proposed model is utilized to forecast hourly electricity load of northern Vietnam and achieves a mean absolute percentage error (MAPE) of 4.57%.

scholarly journals Short-Term Load Forecasting Based on the Transformer Model

Information ◽  
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.

scholarly journals Short-Term Load Forecasting Using Convolutional Neural Networks in COVID-19 Context: The Romanian Case Study

Energies ◽  
2021 ◽  
Vol 14 (13) ◽  
pp. 4046
Author(s):  
Andrei M. Tudose ◽  
Irina I. Picioroaga ◽  
Dorian O. Sidea ◽  
Constantin Bulac ◽  
Valentin A. Boicea
Keyword(s):  
Power Systems ◽  
Mean Squared Error ◽  
Load Forecasting ◽  
Absolute Error ◽  
Percentage Error ◽  
Linear Regression Method ◽  
Short Term ◽  
Short Term Load Forecasting ◽  
The Mean ◽  
The Impact

Short-term load forecasting (STLF) is fundamental for the proper operation of power systems, as it finds its use in various basic processes. Therefore, advanced calculation techniques are needed to obtain accurate results of the consumption prediction, taking into account the numerous exogenous factors that influence the results’ precision. The purpose of this study is to integrate, additionally to the conventional factors (weather, holidays, etc.), the current aspects regarding the global COVID-19 pandemic in solving the STLF problem, using a convolutional neural network (CNN)-based model. To evaluate and validate the impact of the new variables considered in the model, the simulations are conducted using publicly available data from the Romanian power system. A comparison study is further carried out to assess the performance of the proposed model, using the multiple linear regression method and load forecasting results provided by the Romanian Transmission System Operator (TSO). In this regard, the Mean Squared Error (MSE), the Mean Absolute Error (MAE), the Mean Absolute Percentage Error (MAPE), and the Root Mean Square Error (RMSE) are used as evaluation indexes. The proposed methodology shows great potential, as the results reveal better error values compared to the TSO results, despite the limited historical data.

scholarly journals Deseasonalisation in Electricity Load Forecasting

2018 ◽  
Vol 7 (4.30) ◽  
pp. 448
Author(s):  
Maria Elena Binti Nor ◽  
Mohd Saifullah Rusiman ◽  
Suliadi Firdaus Sufahani ◽  
Mohd Asrul Affendi Abdullah ◽  
Sathwinee A/P Bataraja ◽  
...  
Keyword(s):  
Moving Average ◽  
Load Forecasting ◽  
Absolute Error ◽  
Exponential Smoothing ◽  
Percentage Error ◽  
Forecast Performance ◽  
Autoregressive Integrated Moving Average ◽  
Electricity Load ◽  
Seasonal Data ◽  
Electricity Load Forecasting

Nowadays, there is an increasing demand for electricity however overproduction of electricity lead to wastage. Therefore, electricity load forecasting plays a crucial role in operation, planning and maintenance of power system. This study was designed to investigate the effect of deseasonalisation on electricity load data forecasting. The daily seasonality in electricity load data was removed and the forecast methods were employed on both the seasonal data and non-seasonal data. Holt Winters method and Seasonal-Autoregressive Integrated Moving Average (SARIMA) methods were used on the seasonal data. Meanwhile, Simple and Double Exponential Smoothing methods as well as Autoregressive Integrated Moving Average (ARIMA) methods were used on the non-seasonal data. The error measurement that were used to assess the forecast performance were mean absolute error (MAE) and mean absolute percentage error (MAPE). The results revealed that both Exponential Smoothing method and Box-Jenkins method produced better forecast for deseasonalised data. Besides, the study proved that Box-Jenkins method was better in forecasting electricity load data for both seasonal and non-seasonal data.

scholarly journals Integrating Long Short-Term Memory and Genetic Algorithm for Short-Term Load Forecasting

Energies ◽  
2019 ◽  
Vol 12 (11) ◽  
pp. 2040 ◽  
Author(s):  
Arpita Samanta Santra ◽  
Jun-Lin Lin
Keyword(s):  
Genetic Algorithm ◽  
Short Term Memory ◽  
Load Forecasting ◽  
Weather Data ◽  
Percentage Error ◽  
Short Term ◽  
Term Memory ◽  
Short Term Load Forecasting ◽  
Electricity Load ◽  
Long Short Term Memory

Electricity load forecasting is an important task for enhancing energy efficiency and operation reliability of the power system. Forecasting the hourly electricity load of the next day assists in optimizing the resources and minimizing the energy wastage. The main motivation of this study was to improve the robustness of short-term load forecasting (STLF) by utilizing long short- term memory (LSTM) and genetic algorithm (GA). The proposed method is novel: LSTM networks are designed to avoid the problem of long-term dependencies, and GA is used to obtain the optimal LSTM’s parameters, which are then applied to predict the hourly electricity load for the next day. The proposed method was trained using actual load and weather data, and the performance results showed that it yielded small mean absolute percentage error on the test data.

Day-ahead load forecasting using improved grey Verhulst model

2020 ◽  
Vol 18 (5) ◽  
pp. 1335-1348
Author(s):  
Ariel Mutegi Mbae ◽  
Nnamdi I. Nwulu
Keyword(s):  
Power Systems ◽  
Unit Commitment ◽  
Load Forecasting ◽  
Forecasting Model ◽  
Short Term ◽  
Content Type ◽  
Dispatch System ◽  
System Maintenance ◽  
Electricity Load ◽  
Generator Unit

Purpose In the daily energy dispatch process in a power system, accurate short-term electricity load forecasting is a very important tool used by spot market players. It is a critical requirement for optimal generator unit commitment, economic dispatch, system security and stability assessment, contingency and ancillary services management, reserve setting, demand side management, system maintenance and financial planning in power systems. The purpose of this study is to present an improved grey Verhulst electricity load forecasting model. Design/methodology/approach To test the effectiveness of the proposed model for short-term load forecast, studies made use of Kenya’s load demand data for the period from January 2014 to June 2019. Findings The convectional grey Verhulst forecasting model yielded a mean absolute percentage error of 7.82 per cent, whereas the improved model yielded much better results with an error of 2.96 per cent. Practical implications In the daily energy dispatch process in a power system, accurate short-term load forecasting is a very important tool used by spot market players. It is a critical ingredient for optimal generator unit commitment, economic dispatch, system security and stability assessment, contingency and ancillary services management, reserve setting, demand side management, system maintenance and financial planning in power systems. The fact that the model uses actual Kenya’s utility data confirms its usefulness in the practical world for both economic planning and policy matters. Social implications In terms of generation and transmission investments, proper load forecasting will enable utilities to make economically viable decisions. It forms a critical cog of the strategic plans for power utilities and other market players to avoid a situation of heavy stranded investment that adversely impact the final electricity prices and the other extreme scenario of expensive power shortages. Originality/value This research combined the use of natural logarithm and the exponential weighted moving average to improve the forecast accuracy of the grey Verhulst forecasting model.

scholarly journals Short-Term Load Forecasting for Electric Vehicle Charging Stations Based on Deep Learning Approaches

2019 ◽  
Vol 9 (9) ◽  
pp. 1723 ◽  
Author(s):  
Juncheng Zhu ◽  
Zhile Yang ◽  
Yuanjun Guo ◽  
Jiankang Zhang ◽  
Huikun Yang
Keyword(s):  
Deep Learning ◽  
Power Systems ◽  
Unit Commitment ◽  
Combustion Engine ◽  
Load Forecasting ◽  
Learning Approaches ◽  
Short Term ◽  
Load Curve ◽  
Short Term Load Forecasting ◽  
Gated Recurrent Units

Short-term load forecasting is a key task to maintain the stable and effective operation of power systems, providing reasonable future load curve feeding to the unit commitment and economic load dispatch. In recent years, the boost of internal combustion engine (ICE) based vehicles leads to the fossil fuel shortage and environmental pollution, bringing significant contributions to the greenhouse gas emissions. One of the effective ways to solve problems is to use electric vehicles (EVs) to replace the ICE based vehicles. However, the mass rollout of EVs may cause severe problems to the power system due to the huge charging power and stochastic charging behaviors of the EVs drivers. The accurate model of EV charging load forecasting is, therefore, an emerging topic. In this paper, four featured deep learning approaches are employed and compared in forecasting the EVs charging load from the charging station perspective. Numerical results show that the gated recurrent units (GRU) model obtains the best performance on the hourly based historical data charging scenarios, and it, therefore, provides a useful tool of higher accuracy in terms of the hourly based short-term EVs load forecasting.

scholarly journals A Short-Term Load Forecasting Method Based on GRU-CNN Hybrid Neural Network Model

2020 ◽  
Vol 2020 ◽  
pp. 1-10 ◽  
Author(s):  
Lizhen Wu ◽  
Chun Kong ◽  
Xiaohong Hao ◽  
Wei Chen
Keyword(s):  
Neural Network ◽  
Power System ◽  
Network Model ◽  
Neural Network Model ◽  
Feature Vector ◽  
Load Forecasting ◽  
Percentage Error ◽  
Short Term ◽  
Hybrid Neural Network ◽  
Short Term Load Forecasting

Short-term load forecasting (STLF) plays a very important role in improving the economy and stability of the power system operation. With the smart meters and smart sensors widely deployed in the power system, a large amount of data was generated but not fully utilized, these data are complex and diverse, and most of the STLF methods cannot well handle such a huge, complex, and diverse data. For better accuracy of STLF, a GRU-CNN hybrid neural network model which combines the gated recurrent unit (GRU) and convolutional neural networks (CNN) was proposed; the feature vector of time sequence data is extracted by the GRU module, and the feature vector of other high-dimensional data is extracted by the CNN module. The proposed model was tested in a real-world experiment, and the mean absolute percentage error (MAPE) and the root mean square error (RMSE) of the GRU-CNN model are the lowest among BPNN, GRU, and CNN forecasting methods; the proposed GRU-CNN model can more fully use data and achieve more accurate short-term load forecasting.

Sign in / Sign up

Export Citation Format

Share Document