Forecasting for demand response in smart grids: An analysis on use of anthropologic and structural data and short term multiple loads forecasting

By Fahad Javed, Naveed Arshad, Fredrik Wallin, Iana Vassileva, Erik Dahlquist. 2012

207 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/32230af30c1890d7dc231e65266c848d7a1c9823

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Citations in the corpus

Day-ahead building-level load forecasts using deep learning vs. traditional time-series techniques (2019, methodology): “Most existing load forecasting models use non-DL techniques, ranging from the simplest multiple linear regression (MLR) [17,18], autoregressive integrated moving average (ARIMA) [19,20], support vector regression (SVR) [21,22] to the most sophisticated arti ﬁ cial neural network (ANN) [23,24].”
Deep belief network based electricity load forecasting: An analysis of Macedonian case (2016, background): “[18,19], or it may be applied to smaller scale such as local areas in a country [20], a region [21,22], a city [23], a campus [24,25], in microgrid environments [26] or a household [27,28], which is one of the fundamental topics in smart grids.”
Artificial intelligence and machine learning approaches to energy demand-side response: A systematic review (2020, background): “Traditional approaches to load and price forecasting in DR include time-series models such as autoregressive (AR), auto-regressive integrated moving average (ARIMA), and exponential smoothing [277].”
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].”
A Survey on Electric Power Demand Forecasting: Future Trends in Smart Grids, Microgrids and Smart Buildings (2014, background): “Javed et al. (2012) [15] show the need for new forecasting models to deal with the requirements set by Demand Response (DR).”