scholarly journals Computational Intelligence on Short-Term Load Forecasting: A Methodological Overview

Energies ◽  
2019 ◽  
Vol 12 (3) ◽  
pp. 393 ◽  
Author(s):  
Seyedeh Fallah ◽  
Mehdi Ganjkhani ◽  
Shahaboddin Shamshirband ◽  
Kwok-wing Chau
Keyword(s):  
Computational Intelligence ◽  
Electricity Market ◽  
Demand Forecasting ◽  
Load Forecasting ◽  
Short Term ◽  
Hybrid Strategy ◽  
Energy Field ◽  
Short Term Load Forecasting ◽  
Scientific Technical ◽  
Potential Factors

Electricity demand forecasting has been a real challenge for power system scheduling in different levels of energy sectors. Various computational intelligence techniques and methodologies have been employed in the electricity market for short-term load forecasting, although scant evidence is available about the feasibility of these methods considering the type of data and other potential factors. This work introduces several scientific, technical rationales behind short-term load forecasting methodologies based on works of previous researchers in the energy field. Fundamental benefits and drawbacks of these methods are discussed to represent the efficiency of each approach in various circumstances. Finally, a hybrid strategy is proposed.

scholarly journals Computational Intelligence Load Forecasting: A Methodological Overview

2018 ◽  
Author(s):  
Narjes Fallah ◽  
Mehdi Ganjkhani
Keyword(s):  
Computational Intelligence ◽  
Electricity Market ◽  
Demand Forecasting ◽  
Load Forecasting ◽  
Hybrid Strategy ◽  
Scientific Technical ◽  
Power System Scheduling ◽  
Potential Factors ◽  
Real Challenge ◽  
Different Levels

Electricity demand forecasting has been a real challenge for power system scheduling in the different levels of the energy sectors. Various computational intelligence techniques and methodologies have been employed in the electricity market for load forecasting; although, scant evidence is available about the feasibility of each of these methods considering the type of data and other potential factors. This work introduces several scientific, technical rationale behind intelligent forecasting methods, based on the work of previous researchers in the field of energy. The fundamental benefits and main drawbacks of the aforementioned methods are discussed in order to depict the efficiency of each approach in various situations. In the end, a proposed hybrid strategy is represented.

scholarly journals Computational Intelligence Load Forecasting: A Methodological Overview

2018 ◽  
Author(s):  
Seyedeh Narjes Fallah ◽  
Mehdi Ganjkhani ◽  
Shahab Shamshirband ◽  
Kwok-wing Chau
Keyword(s):  
Computational Intelligence ◽  
Electricity Market ◽  
Demand Forecasting ◽  
Load Forecasting ◽  
Hybrid Strategy ◽  
Scientific Technical ◽  
Power System Scheduling ◽  
Potential Factors ◽  
Real Challenge ◽  
Different Levels

Electricity demand forecasting has been a real challenge for power system scheduling in the different levels of the energy sectors. Various computational intelligence techniques and methodologies have been employed in the electricity market for load forecasting; although, scant evidence is available about the feasibility of each of these methods considering the type of data and other potential factors. This work introduces several scientific, technical rationale behind intelligent forecasting methods, based on the work of previous researchers in the field of energy. The fundamental benefits and main drawbacks of the aforementioned methods are discussed in order to depict the efficiency of each approach in various situations. In the end, a proposed hybrid strategy is represented.

Design of a Short-Term Load Forecasting System for Small Hydropower in the Context of Electricity Market

2013 ◽  
Vol 330 ◽  
pp. 178-182
Author(s):  
Shou Qiang Fu ◽  
Min Xiang Huang ◽  
Fei Fei Sun
Keyword(s):  
Electricity Market ◽  
Wavelet Decomposition ◽  
Load Forecasting ◽  
Hydropower Generation ◽  
Processing Load ◽  
Short Term ◽  
Small Hydropower ◽  
Information Monitoring ◽  
Short Term Load Forecasting ◽  
Forecasting System

On the basis of the analysis of influencing factors on small hydropower generation load, considering the characteristics of small hydropower load, this paper presents a short-term load forecasting system for small hydropower in the context of electricity market. It is composed of the following components: information collection and processing, load forecasting, information monitoring. The system uses a method to segment and cluster the load curves, then wavelet decomposition is applied to load data, and a complex forecasting model is taken. Meanwhile, fulfill feedback control through the part of information monitoring, and extended short-term load forecasting is introduced. The system can improve the overall level of short-term load forecasting for small hydropower.

scholarly journals A Hybrid Approach to Short-Term Load Forecasting Aimed at Bad Data Detection in Secondary Substation Monitoring Equipment

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3947 ◽  
Author(s):  
Pedro Martín ◽  
Guillermo Moreno ◽  
Francisco Javier Rodríguez ◽  
José Antonio Jiménez ◽  
Ignacio Fernández
Keyword(s):  
State Estimation ◽  
Measurement Errors ◽  
Load Forecasting ◽  
The State ◽  
Short Term ◽  
Monitoring Equipment ◽  
Hybrid Strategy ◽  
Power Load ◽  
Short Term Load Forecasting ◽  
Bad Data

Bad data as a result of measurement errors in secondary substation (SS) monitoring equipment is difficult to detect and negatively affects power system state estimation performance by both increasing the computational burden and jeopardizing the state estimation accuracy. In this paper a short-term load forecasting (STLF) hybrid strategy based on singular spectrum analysis (SSA) in combination with artificial neural networks (ANN), is presented. This STLF approach is aimed at detecting, identifying and eliminating and/or correcting such bad data before it is provided to the state estimator. This approach is developed to improve the accuracy of the load forecasts and it is tested against real power load data provided by electricity suppliers. Depending on the week considered, mean absolute percentage error (MAPE) values which range from 1.6% to 3.4% are achieved for STLF. Different systematic errors, such as gain and offset error levels and outliers, are successfully detected with a hit rate of 98%, and the corresponding measurements are corrected before they are sent to the control center for state estimation purposes.

scholarly journals A New Strategy for Short-Term Load Forecasting

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Yi Yang ◽  
Jie Wu ◽  
Yanhua Chen ◽  
Caihong Li
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Arima Model ◽  
Demand Forecasting ◽  
Load Forecasting ◽  
Short Term ◽  
Load Demand ◽  
Short Term Load Forecasting ◽  
New Strategy ◽  
Residual Series

Electricity is a special energy which is hard to store, so the electricity demand forecasting remains an important problem. Accurate short-term load forecasting (STLF) plays a vital role in power systems because it is the essential part of power system planning and operation, and it is also fundamental in many applications. Considering that an individual forecasting model usually cannot work very well for STLF, a hybrid model based on the seasonal ARIMA model and BP neural network is presented in this paper to improve the forecasting accuracy. Firstly the seasonal ARIMA model is adopted to forecast the electric load demand day ahead; then, by using the residual load demand series obtained in this forecasting process as the original series, the follow-up residual series is forecasted by BP neural network; finally, by summing up the forecasted residual series and the forecasted load demand series got by seasonal ARIMA model, the final load demand forecasting series is obtained. Case studies show that the new strategy is quite useful to improve the accuracy of STLF.

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