Neural networks for short-term load forecasting: a review and evaluation

By Henrique S. Hippert, Carlos Eduardo Pedreira, Reinaldo Castro Souza. 2001

2,323 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Electrical and Electronic Engineering, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/9957817bf8ee72a72768cb4303ddcfc9ed1227b6

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Citations in the corpus

Toward Automatic Time-Series Forecasting Using Neural Networks (2012, background): “overview papers, for example, [1], [8], [17], provide a good”
Short-Term Load Forecasting Methods: An Evaluation Based on European Data (2007, background): “Artificial neural networks (ANNs) have featured prominently in the load forecasting literature (see [10]).”
A High Precision Artificial Neural Networks Model for Short-Term Energy Load Forecasting (2018, methodology): “Some of frequently used load forecast methods include linear regression [5,6,20], autoregressive methods [7,21], and artificial neural networks [9,22,23].”
Forecasting and operational research: a review (2008, background): “Realtime data acquisition and processing in areas as diverse as stock-price trading, electricity load forecasting (Hippert et al, 2001), weather forecasting or water resource management do, however, use computer power intensively in their forecasting and could be applied more widely.”
25 years of time series forecasting (2006, methodology): “One major application area of ANNs is forecasting; see Zhang et al. (1998) and Hippert et al. (2001) for good surveys of the literature.”
A Novel Approach to Forecast Electricity Price for PJM Using Neural Network and Similar Days Method (2007, methodology): “ANNs were used for solving the short-term load forecast problems [12], [17]–[20].”
Triple seasonal methods for short-term electricity demand forecasting (2010, background): “Keywords: Forecasting; Electricity demand; Seasonality; Exponential smoothing; ARMA.”
Short-Term Load and Wind Power Forecasting Using Neural Network-Based Prediction Intervals (2014, methodology): “Forecasting models that are popularly applied to electrical load and the renewable energy forecasting can be classified into three categories: 1) statistical models, such as auroregressive (AR), AR integrated moving average (ARIMA), and exponential smoothing (ES) models [1], [3], [4]; 2) artificial ”
Deep Learning for Time Series Forecasting: The Electric Load Case (2019, background): “Since deregulation of electric energy distribution and wide adoption of renewables strongly affects daily market prices, STLF emerges to be of fundamental importance for efficient power supply [3].”
Forecasting intraday time series with multiple seasonal cycles using parsimonious seasonal exponential smoothing (2009, methodology): “Much research has focused on the use of ANNs to model electricity load in terms of weather variables [25].”
A comparison of univariate methods for forecasting electricity demand up to a day ahead (2006, background): “Although some success has been reported with the use of applied neural networks for univariate short term load forecasting (e.g., Hippert et al., 2001), there remains little systematic evidence in favour of a particular architecture, nor on the amount of training data that should be used (Rui &…”
Functional time series approach for forecasting very short-term electricity demand (2013, methodology): “Because an NN is able to capture the nonlinear and non-parametric features, it is an attractive tool for modelling loads in terms of weather variables (see [19]).”
Optimal Deep Learning LSTM Model for Electric Load Forecasting using Feature Selection and Genetic Algorithm: Comparison with Machine Learning Approaches † (2018, background): “They ranged from classical time series analysis to recent machine learning approaches [15–20].”