Electric power load in Brazil: view on the long-term forecasting models

Energy efficiency and renewable energy are the two main research topics for sustainable energy. In the past ten years, countries around the world have invested a lot of manpower into new energy research. However, in addition to new energy development, energy efficiency technologies need to be emphasized to promote production efficiency and reduce environmental pollution. In order to improve power production efficiency, an integrated solution regarding the issue of electric power load forecasting was proposed in this study. The solution proposed was to, in combination with persistence and search algorithms, establish a new integrated ultra-short-term electric power load forecasting method based on the adaptive-network-based fuzzy inference system (ANFIS) and back-propagation neural network (BPN), which can be applied in forecasting electric power load in Taiwan. The research methodology used in this paper was mainly to acquire and process the all-day electric power load data of Taiwan Power and execute preliminary forecasting values of the electric power load by applying ANFIS, BPN and persistence. The preliminary forecasting values of the electric power load obtained therefrom were called suboptimal solutions and finally the optimal weighted value was determined by applying a search algorithm through integrating the above three methods by weighting. In this paper, the optimal electric power load value was forecasted based on the weighted value obtained therefrom. It was proven through experimental results that the solution proposed in this paper can be used to accurately forecast electric power load, with a minimal error.

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Medium and Long-Term Forecasting Method of China’s Power Load Based on SaDE-SVM Algorithm

Communications in Computer and Information Science - Bio-inspired Computing: Theories and Applications ◽

10.1007/978-981-13-2826-8_42 ◽

2018 ◽

pp. 484-495

Author(s):

Yuansheng Huang ◽

Lijun Zhang ◽

Mengshu Shi ◽

Shijian Liu ◽

Siyuan Xu

Keyword(s):

Power Load ◽

Forecasting Method ◽

Svm Algorithm ◽

Long Term Forecasting

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Building electric power load assessment

Building Services Design for Energy-Efficient Buildings ◽

10.1201/9781351261166-13 ◽

2020 ◽

pp. 307-332

Author(s):

Paul Tymkow ◽

Savvas Tassou ◽

Maria Kolokotroni ◽

Hussam Jouhara

Keyword(s):

Electric Power ◽

Power Load ◽

Electric Power Load

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

Applied Sciences ◽

10.3390/app10186489 ◽

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.

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Birthquake: The Baby Boom and Its Aftershocks. By Diane J. Macunovich. Chicago: The University of Chicago Press, 2002. Pp xiii, 314. $37.50.

The Journal of Economic History ◽

10.1017/s0022050703541808 ◽

2003 ◽

Vol 63 (1) ◽

pp. 295-296

Author(s):

Judith Woerner Mills

Keyword(s):

Baby Boom ◽

Relative Size ◽

Economic Behavior ◽

Cohort Size ◽

Age Composition ◽

Forecasting Models ◽

Long Term Forecasting ◽

The University ◽

University Of Chicago Press

Before I had even finished reading the first chapter of Diane Macunovich's new book, three things were crystal clear:People matter: a society's demographics need to be considered explicitly when trying to understand or to forecast its economic behavior.Einstein's conclusions about relativity apply to economies: changes in the relative size and age composition of a population can lead to major changes in its social and economic behavior.Economic demographers rule! From now on, users of long-term forecasting models will need to include information on changes in age structure and cohort size if they wish to forecast events more than a few years ahead.

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