Probability Forecasting for Short-term Electricity Load Based on LSTM

2019 ◽  
Author(s):  
Yaqian Fan ◽  
Fang Fang ◽  
Xin Wang
Keyword(s):  
Short Term ◽  
Probability Forecasting ◽  
Electricity Load

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

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

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 ARTIFICIAL NEURAL NETWORK BASE SHORT-TERM ELECTRICITY LOAD FORECASTING: A CASE STUDY OF A 132/33KV TRANSMISSION SUB-STATION

2020 ◽  
Vol 10 (2) ◽  
pp. 200-205
Author(s):  
Isaac Adekunle Samuel ◽  
Segun Ekundayo ◽  
Ayokunle Awelewa ◽  
Tobiloba Emmanuel Somefun ◽  
Adeyinka Adewale
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Load Forecasting ◽  
Short Term ◽  
Electricity Load ◽  
Artificial Neural ◽  
Electricity Load Forecasting
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