Journal ArticleDOI
A review on artificial intelligence based load demand forecasting techniques for smart grid and buildings
Reads0
Chats0
TLDR
In this article, the authors provide a comprehensive and systematic literature review of Artificial Intelligence based short-term load forecasting techniques and provide the major objective of this study is to review, identify, evaluate and analyze the performance of artificial Intelligence based load forecast models and research gaps.Abstract:
Electrical load forecasting plays a vital role in order to achieve the concept of next generation power system such as smart grid, efficient energy management and better power system planning. As a result, high forecast accuracy is required for multiple time horizons that are associated with regulation, dispatching, scheduling and unit commitment of power grid. Artificial Intelligence (AI) based techniques are being developed and deployed worldwide in on Varity of applications, because of its superior capability to handle the complex input and output relationship. This paper provides the comprehensive and systematic literature review of Artificial Intelligence based short term load forecasting techniques. The major objective of this study is to review, identify, evaluate and analyze the performance of Artificial Intelligence (AI) based load forecast models and research gaps. The accuracy of ANN based forecast model is found to be dependent on number of parameters such as forecast model architecture, input combination, activation functions and training algorithm of the network and other exogenous variables affecting on forecast model inputs. Published literature presented in this paper show the potential of AI techniques for effective load forecasting in order to achieve the concept of smart grid and buildings.read more
Citations
More filters
Journal ArticleDOI
Forecasting of photovoltaic power generation and model optimization: A review
Utpal Kumar Das,Kok Soon Tey,Mohammadmehdi Seyedmahmoudian,Saad Mekhilef,Moh Yamani Idna Idris,Willem Van Deventer,Ben Horan,Alex Stojcevski +7 more
TL;DR: In this paper, a comprehensive and systematic review of the direct forecasting of PV power generation is presented, where the importance of the correlation of the input-output data and the preprocessing of model input data are discussed.
Journal ArticleDOI
A review on time series forecasting techniques for building energy consumption
TL;DR: The various combinations of the hybrid model are found to be the most effective in time series energy forecasting for building and the nine most popular forecasting techniques based on the machine learning platform are analyzed.
Journal ArticleDOI
A review and evaluation of the state-of-the-art in PV solar power forecasting:Techniques and optimization
TL;DR: In this paper, the authors reviewed and evaluated contemporary forecasting techniques for photovoltaics into power grids, and concluded that ensembles of artificial neural networks are best for forecasting short-term PV power forecast and online sequential extreme learning machine superb for adaptive networks; while Bootstrap technique optimum for estimating uncertainty.
Posted Content
Tackling Climate Change with Machine Learning
David Rolnick,Priya L. Donti,Lynn H. Kaack,K. Kochanski,Alexandre Lacoste,Kris Sankaran,Andrew S. Ross,Nikola Milojevic-Dupont,Natasha Jaques,Anna Waldman-Brown,Alexandra Luccioni,Tegan Maharaj,Evan D. Sherwin,S. Karthik Mukkavilli,Konrad P. Kording,Carla P. Gomes,Andrew Y. Ng,Demis Hassabis,John Platt,Felix Creutzig,Jennifer Chayes,Yoshua Bengio +21 more
TL;DR: From smart grids to disaster management, high impact problems where existing gaps can be filled by ML are identified, in collaboration with other fields, to join the global effort against climate change.
Journal ArticleDOI
Short-Term Load Forecasting Using EMD-LSTM Neural Networks with a Xgboost Algorithm for Feature Importance Evaluation
TL;DR: A hybrid algorithm that combines similar days (SD) selection, empirical mode decomposition (EMD), and long short-term memory (LSTM) neural networks to construct a prediction model (i.e., SD-EMD-L STM) for short- term load forecasting is presented.
References
More filters
Book
Generalized Linear Models
Peter McCullagh,John A. Nelder +1 more
TL;DR: In this paper, a generalization of the analysis of variance is given for these models using log- likelihoods, illustrated by examples relating to four distributions; the Normal, Binomial (probit analysis, etc.), Poisson (contingency tables), and gamma (variance components).
Journal ArticleDOI
Neural networks and physical systems with emergent collective computational abilities
TL;DR: A model of a system having a large number of simple equivalent components, based on aspects of neurobiology but readily adapted to integrated circuits, produces a content-addressable memory which correctly yields an entire memory from any subpart of sufficient size.
Journal ArticleDOI
A logical calculus of the ideas immanent in nervous activity
Warren S. McCulloch,Walter Pitts +1 more
TL;DR: In this article, it is shown that many particular choices among possible neurophysiological assumptions are equivalent, in the sense that for every net behaving under one assumption, there exists another net which behaves under another and gives the same results, although perhaps not in the same time.