NDLI: Mid-term electricity load forecasting by a new composite method based on optimal learning MLP algorithm

Content Provider	IET Digital Library
Author	Askari, Mostafa Keynia, Farshid
Abstract	Electricity load forecasting has been developed as an important issue in the deregulated power system in recent years. Many researchers have been working on the prediction of daily peak load for next month as an important type of mid-term load forecasting (MTLF). Nowadays, MTLF provides useful information for assessing environmental impacts, maintenance scheduling, adequacy assessment, scheduling of fuel supplies and limited energy resources etc. The characteristics of mid-term load signal, such as its non-stationary, volatile and non-linear behaviour, present serious challenges for this forecasting. On the other hand, many input variables and relative parameters can affect the load pattern. In this study, a new composite method based on a multi-layer perceptron neural network and optimisation techniques has been proposed to solve the MTLF problem. The proposed method has an optimal training algorithm composed of two search algorithms (particle swarm optimisation and improved ant lion optimiser) and a multi-layer perceptron neural network. The accuracy of the proposed forecast method is extensively evaluated based on several benchmark datasets.
Starting Page	845
Ending Page	852
Page Count	8
ISSN	17518687
Volume Number	14
e-ISSN	17518695
Issue Number	Issue 5, Mar (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-gtd/14/5
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-gtd.2019.0797
Journal	IET Generation, Transmission & Distribution
Publisher Date	2019-12-09
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Composite Method Daily Peak Load Deregulated Power System Environmental Impacts Forecast Method Fuel Supplies Improved Ant Lion Optimiser Learning in AI Limited Energy ReSource Load Forecasting Load Pattern Maintenance Scheduling Mid-term Electricity Load Forecasting Mid-term Load Forecasting Mid-term Load Signal MTLF Problem Multilayer Perceptron Neural Network Multilayer Perceptrons Neural Computing Technique Optimal Learning MLP Algorithm Optimal Training Algorithm Optimisation Technique Particle Swarm Optimisation Power Engineering Computing Power System Planning And Layout Search Algorithm Search Problem
Content Type	Text
Resource Type	Article
Subject	Control and Systems Engineering Energy Engineering and Power Technology Electrical and Electronic Engineering

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