A review on artificial intelligence based load demand forecasting techniques for smart grid and buildings

By Muhammad Qamar Raza, Abbas Khosravi. 2015

924 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Electrical and Electronic Engineering

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Citations in the corpus

A survey of data fusion in smart city applications (2019, background): “It has been extensively studied in [25], [162]–[164] and the current trend is to optimize the building resources such as hot water systems, electrical consumption, and heating ventilation & air conditioning (HVAC).”
A High Precision Artificial Neural Networks Model for Short-Term Energy Load Forecasting (2018, background): “The smart grids [1] are particularly important as they enable several other low-carbon energy technologies [2], including electric vehicles, variable renewable energy sources, and demand response.”
Artificial intelligence and machine learning approaches to energy demand-side response: A systematic review (2020, background): “The reader interested in the wider spectrum of load forecasting in the smart grid context can refer to the review of Raza and Khosravi [18].”
A novel stacked generalization ensemble-based hybrid LGBM-XGB-MLP model for Short-Term Load Forecasting (2021, background): “Neural network architectures include many types of algorithms with different designs to suits well-defined applications [24,25].”
Recent advances in electricity price forecasting: A review of probabilistic forecasting (2016, background): “In particular, as Raza and Khosravi [10] argue, the electricity price is one of the influential explanatory variables in load forecasting and PEPFs could be considered as input in probabilistic load forecasting models for smart grids and buildings.”
A review of machine learning in building load prediction (2021, neutral): “The paper written by Raza and Khosravi [17] focused on the architecture of ANN.”
Review on forecasting of photovoltaic power generation based on machine learning and metaheuristic techniques (2019, methodology): “These combinations are (i) genetic algorithms (GAs) and ANNs, (ii) fuzzy and ANNs, (iii) ANFIS, (iv) ANNs and physical model, (v) ANNs and ARMA, (vi) wavelets and ANNs, (vii) ANN and optimisation algorithms, (viii) WT and SVM, (ix) SVM and optimisation algorithms, and (x) seasonal auto-regressive in”