A review on artificial intelligence based load demand forecasting techniques for smart grid and buildings
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.
- Publication:
-
Renewable and Sustainable Energy Reviews
- Pub Date:
- October 2015
- DOI:
- 10.1016/j.rser.2015.04.065
- Bibcode:
- 2015RSERv..50.1352R
- Keywords:
-
- AI;
- Artificial Intelligence;
- ANN;
- Artificial neural network;
- AR;
- Auto-Regressive;
- ARIMA;
- Auto-Regressive Integrated Moving Average;
- ARMA;
- Auto-Regressive Moving Average;
- DR;
- Demand Response;
- DG;
- Distributed Generation;
- SG;
- Smart Grid;
- SB;
- Smart Building;
- HS;
- Hybrid System;
- GA;
- Genetic Algorithm;
- ES;
- Expert System;
- TS;
- Time Series;
- SVM;
- Support Vector Machine;
- AIS;
- Artificial Immune System;
- WNN;
- Wavelet Neural Network;
- BP;
- Backpropagation;
- LM;
- Levenberg–Marquardt;
- ISO;
- Independent System Operator;
- MAE;
- Mean Absolute Error;
- RMSE;
- Root Mean Square Error;
- MAPE;
- Mean Absolute Percent Error;
- MLP;
- Multi-Layer Perceptron;
- MR;
- MADALINE adaptation Rule;
- R;
- Correlation Coefficient;
- Artificial intelligence (AI);
- Neural network (NN);
- Fuzzy logic;
- Short term load forecasting (STLF);
- Smart grid (SG)