Short‐term electric power load forecasting using factor analysis and long short‐term memory for smart cities

2020 ◽  
Author(s):  
Venkataramana Veeramsetty ◽  
D. Rakesh Chandra ◽  
Surender Reddy Salkuti
Keyword(s):  
Factor Analysis ◽  
Short Term Memory ◽  
Smart Cities ◽  
Load Forecasting ◽  
Short Term ◽  
Term Memory ◽  
Power Load ◽  
Power Load Forecasting ◽  
Long Short Term Memory ◽  
Electric Power Load

scholarly journals Short-term Power Load Forecast of an Electrically Heated House in St. John’s, Newfoundland, Canada

2020 ◽  
Vol 4 (3) ◽  
Author(s):  
Hla U May Marma ◽  
M. Tariq Iqbal ◽  
Christopher Thomas Seary
Keyword(s):  
Neural Network ◽  
Short Term Memory ◽  
Load Forecasting ◽  
Weather Conditions ◽  
Short Term ◽  
Forecasting Accuracy ◽  
Term Memory ◽  
Power Load ◽  
Power Load Forecasting ◽  
Long Short Term Memory

A highly efficient deep learning method for short-term power load forecasting has been developed recently. It is a challenge to improve forecasting accuracy, as power consumption data at the individual household level is erratic for variable weather conditions and random human behaviour.  In this paper, a robust short-term power load forecasting method is developed based on a Bidirectional long short-term memory (Bi-LSTM) and long short-term memory (LSTM) neural network with stationary wavelet transform (SWT). The actual power load data is classified according to seasonal power usage behaviour. For each load classification, short-term power load forecasting is performed using the developed method. A set of lagged power load data vectors is generated from the historical power load data, and SWT decomposes the vectors into sub-components. A Bi-LSTM neural network layer extracts features from the sub-components, and an LSTM layer is used to forecast the power load from each extracted feature. A dropout layer with fixed probability is added after the Bi-LSTM and LSTM layers to bolster the forecasting accuracy. In order to evaluate the accuracy of the proposed model, it is compared against other developed short-term load forecasting models which are subjected to two seasonal load classifications.

Short-term power load forecasting using integrated methods based on long short-term memory

2020 ◽  
Vol 63 (4) ◽  
pp. 614-624
Author(s):  
WenJie Zhang ◽  
Jian Qin ◽  
Feng Mei ◽  
JunJie Fu ◽  
Bo Dai ◽  
...  
Keyword(s):  
Short Term Memory ◽  
Load Forecasting ◽  
Short Term ◽  
Term Memory ◽  
Power Load ◽  
Power Load Forecasting ◽  
Integrated Methods ◽  
Long Short Term Memory

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.

scholarly journals Forecasting for Ultra-Short-Term Electric Power Load Based on Integrated Artificial Neural Networks

Symmetry ◽  
2019 ◽  
Vol 11 (8) ◽  
pp. 1063 ◽  
Author(s):  
Horng-Lin Shieh ◽  
Fu-Hsien Chen
Keyword(s):  
Energy Efficiency ◽  
Electric Power ◽  
Production Efficiency ◽  
Load Forecasting ◽  
Short Term ◽  
Inference System ◽  
Power Load ◽  
Power Load Forecasting ◽  
New Energy ◽  
Electric Power Load

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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