scholarly journals An Incremental Deep Model For Computing Electrical Power Load Forecasting Based Social Factors

2020 ◽  
Vol 9 (6) ◽  
pp. 43-50
Keyword(s):  
Electrical Power ◽  
Load Forecasting ◽  
Electrical Load ◽  
Factors Affecting ◽  
Deep Model ◽  
Power Load ◽  
Influent Factors ◽  
Consumption Demand

Load forecasting (LF) is critical for guaranteeing adequate limit and controlling of the power business in numerous nations, which theeconomies dependingon electricity. Its production (load) and consumption (demand) have to be in equilibrium at all times since storing electricity, in a considerable quantity, results in high costs. Therefore, the forecasting of the electrical load problem in many countries become crucial and critical in the recent years. In this paper, a novel deep model architecture for LFintroduced, which integrates the features of dataset in discovering the most influent factors affecting electrical load usage. In addition, different LF strategies introduced and their interrelations just asthe intensity of neural organizations to rough the heap estimating. The deep model is based on in three terms time: Long-term (yearly), Mid-term (Monthly), and Mid-term (Weekly), which can possibly provide interrelated deep learning models. Moreover, to generating more accurate predictions based the hierarchal learning architecture. The dataset used is introduced in the case study, which is power load in Giga-watt from years 2006 to 2015. The load forecasted for the year 2016 and is validated to check its accuracy

An Intelligent Hybrid Model for Bus Load Forecasting in Electrical Short-Term Operation Tasks

2012 ◽  
pp. 540-562
Author(s):  
Ricardo Menezes Salgado ◽  
Takaaki Ohishi ◽  
Rosangela Ballini
Keyword(s):  
Power System ◽  
Hybrid Model ◽  
Electrical Power ◽  
Load Forecasting ◽  
Electrical Power System ◽  
Term Operation ◽  
Operating Load ◽  
Conventional Models ◽  
Safe Operating

The main objective of this chapter is to present a hybrid model for bus load forecasting. This approach represents an essential tool for the operation of the electrical power system and the hybrid model combines a bus clustering process and a load forecasting model. As a case study, the model was applied to the real Brazilian electrical system, and the results revealed a performance similar to that of conventional models for bus load forecasting, but about 14 times faster. The results are compatible with the safe operating load levels for the Brazilian electrical power system and have proved to be adequate for use in real operation tasks.

scholarly journals Deep Neural Network and Long Short-Term Memory for Electric Power Load Forecasting

2020 ◽  
Vol 10 (18) ◽  
pp. 6489
Author(s):  
Namrye Son ◽  
Seunghak Yang ◽  
Jeongseung Na
Keyword(s):  
Power Consumption ◽  
Short Term Memory ◽  
Load Forecasting ◽  
Power Demand ◽  
Short Term ◽  
Power Load ◽  
Short And Long Term ◽  
Long Short Term Memory ◽  
Electric Power Load

Forecasting domestic and foreign power demand is crucial for planning the operation and expansion of facilities. Power demand patterns are very complex owing to energy market deregulation. Therefore, developing an appropriate power forecasting model for an electrical grid is challenging. In particular, when consumers use power irregularly, the utility cannot accurately predict short- and long-term power consumption. Utilities that experience short- and long-term power demands cannot operate power supplies reliably; in worst-case scenarios, blackouts occur. Therefore, the utility must predict the power demands by analyzing the customers’ power consumption patterns for power supply stabilization. For this, a medium- and long-term power forecasting is proposed. The electricity demand forecast was divided into medium-term and long-term load forecast for customers with different power consumption patterns. Among various deep learning methods, deep neural networks (DNNs) and long short-term memory (LSTM) were employed for the time series prediction. The DNN and LSTM performances were compared to verify the proposed model. The two models were tested, and the results were examined with the accuracies of the six most commonly used evaluation measures in the medium- and long-term electric power load forecasting. The DNN outperformed the LSTM, regardless of the customer’s power pattern.

The Factors Affecting Accuracy Rate of Power Load Forecasting in Summer

2014 ◽  
Vol 1049-1050 ◽  
pp. 617-620
Author(s):  
Yan Ping Chen ◽  
Yan Li Chang
Keyword(s):  
Low Power ◽  
Load Forecasting ◽  
Paper Analysis ◽  
Accuracy Rate ◽  
Forecasting Accuracy ◽  
Factors Affecting ◽  
Power Load ◽  
Power Load Forecasting

This paper analysis the low power load forecasting accuracy in summer deep. It found the factors affecting accuracy rate of power load forecasting in summer, and proposed the measures to increase the load forecasting accuracy.

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