A Novel Cloud Theory Based Time-series Predictive Method for Middle-term Electric Load Forecasting

2006 ◽  
Author(s):  
X.m. Yang ◽  
J.s. Yuan ◽  
H.n. Mao ◽  
J.y. Yuan
Keyword(s):  
Time Series ◽  
Load Forecasting ◽  
Electric Load ◽  
Middle Term ◽  
Predictive Method ◽  
Cloud Theory ◽  
Electric Load Forecasting

scholarly journals Single and Multi-Sequence Deep Learning Models for Short and Medium Term Electric Load Forecasting

Energies ◽  
2019 ◽  
Vol 12 (1) ◽  
pp. 149 ◽  
Author(s):  
Salah Bouktif ◽  
Ali Fiaz ◽  
Ali Ouni ◽  
Mohamed Adel Serhani
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Deep Learning ◽  
Short Term Memory ◽  
Load Forecasting ◽  
Forecasting Model ◽  
Time Lags ◽  
Electric Load ◽  
Learning Approaches ◽  
Electric Load Forecasting

Time series analysis using long short term memory (LSTM) deep learning is a very attractive strategy to achieve accurate electric load forecasting. Although it outperforms most machine learning approaches, the LSTM forecasting model still reveals a lack of validity because it neglects several characteristics of the electric load exhibited by time series. In this work, we propose a load-forecasting model based on enhanced-LSTM that explicitly considers the periodicity characteristic of the electric load by using multiple sequences of inputs time lags. An autoregressive model is developed together with an autocorrelation function (ACF) to regress consumption and identify the most relevant time lags to feed the multi-sequence LSTM. Two variations of deep neural networks, LSTM and gated recurrent unit (GRU) are developed for both single and multi-sequence time-lagged features. These models are compared to each other and to a spectrum of data mining benchmark techniques including artificial neural networks (ANN), boosting, and bagging ensemble trees. France Metropolitan’s electricity consumption data is used to train and validate our models. The obtained results show that GRU- and LSTM-based deep learning model with multi-sequence time lags achieve higher performance than other alternatives including the single-sequence LSTM. It is demonstrated that the new models can capture critical characteristics of complex time series (i.e., periodicity) by encompassing past information from multiple timescale sequences. These models subsequently achieve predictions that are more accurate.

Daily Load Forecasting Based on RBF-ARX Model

2014 ◽  
Vol 623 ◽  
pp. 48-56
Author(s):  
Hai Liang Hou ◽  
Jie Chen ◽  
Miao Ping Sun ◽  
Bin Jun Cai
Keyword(s):  
Time Series ◽  
Load Forecasting ◽  
Optimization Method ◽  
Parameters Optimization ◽  
Model Parameters ◽  
Electric Load ◽  
Short Term ◽  
Arx Model ◽  
Short Term Load Forecasting ◽  
Electric Load Forecasting

Short-term load forecasting plays a vital role for the power system’s safe operation and production arrangements. This paper regards electric load forecasting as a nonlinear time series prediction problem, and establishes the electric load autoregressive prediction model (ARX Model) based on historical load data, approaching ARX model parameters with RBF neural network, estimating model parameters with using a Structured Nonlinear Parameters Optimization Method (SNPOM), proposing cycle prediction method for short-term electric load forecasting. Experimental results show that the method has higher prediction accuracy for short-term electric load forecasting. Keywords: Short-term load forecasting; time series; RBF-ARX model; cycle forecasting; structured nonlinear parameter optimization method

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