Abstract

Improved-performance day-ahead electricity demand forecast is important to deliver necessary information for right decision of energy management of microgrids. It supports microgrid operators and stakeholders to have better decisions on microgrid flexibility, stability and control. The available conventional forecasting methods for electricity demand at national or regional level are not effective for electricity demand forecasting in microgrids. This is due to the fact that the electricity consumption in microgrids is many times less than the regional or national demands and it is highly volatile. In this paper, an integrated Artificial Intelligence (AI) based approach consisting of Wavelet Transform (WT), Simulated Annealing (SA) and Feedforward Artificial Neural Network (FFANN) is devised for day-ahead prediction of electric power consumption in microgrids. The FFANN is the basic forecasting engine of the proposed model. The WT is utilized to extract relevant features of the target variable (electric load data series) to obtain a cluster of enhanced-feature subseries. The extracted subseries of the past values of the electric load demand data are employed as the target variables to model the FFANN. The SA optimization technique is employed to obtain the optimal values of the FFANN weight parameters during the training process. Historical information of actual electricity consumption, meteorological variables, daily variations, weekly variations, and working/non-working day indicators have been employed to develop the forecasting tool of the devised integrated AI based approach. The approach is validated using electricity demand data of an operational microgrid in Beijing, China. The prediction results are presented for future testing days with one-hour time interval. The validation results demonstrated that the devised approach is capable to forecast the microgrid electricity demand with acceptably small error and reasonably short computation time. Moreover, the prediction performance of the devised approach has been evaluated relative to other four approaches and resulted in better prediction accuracy.

Highlights

  • In the conventional electric power system energy is produced by huge generation plants installed far away from load points

  • (back-propagation feedforward Artificial neural networks (ANNs)), Genetic Algorithm (GA)-Feedforward Artificial Neural Network (FFANN) (GA combined with FFANN), and Particle Swarm Optimization (PSO)-FFANN (PSO combined with FFANN), to demonstrate its robustness regarding prediction accuracy and other performance indexes

  • The hybrid Wavelet Transform (WT)-Simulated Annealing (SA)-FFANN Artificial Intelligence (AI) model is established for day-ahead electric load prediction in microgrids

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Summary

Introduction

In the conventional electric power system energy is produced by huge generation plants installed far away from load points. Most of the prior Feedforward Artificial Neural Network (FFANN) based load demand forecasting approaches have used a Back Propagation (BP) learning method to obtain the FFANN weight parameters. A new and effective AI-based day-ahead microgrid electricity demand forecasting approach using the integration of FFANN, SA and WT is devised. The devised WT-SA-FFANN based integrated electricity demand forecasting approach is compared with Persistence, BP-FFANN (back-propagation feedforward ANN), Genetic Algorithm (GA)-FFANN (GA combined with FFANN), and Particle Swarm Optimization (PSO)-FFANN (PSO combined with FFANN), to demonstrate its robustness regarding prediction accuracy and other performance indexes. Provide a new and effective AI-based hybrid method for day-ahead electric load prediction in microgrids considering actual electricity demand, meteorological variables and other derivative data and input factors; Improve electricity demand forecasting accuracy; Deliver a hands-on solution to electricity demand prediction problems in microgrids and other small-scale energy systems.

Proposed 24 h-Ahead Electricity Demand Prediction Strategy
Schematic
Forecasting Accuracy Assessment
X a f 2
Experimental Results and Discussions
Data Sources and Treatment
Electric Load Data
Weather Data
Data Preparation
Proposed Framework for the AI-Based WT-SA-FFANN Integrated Forecasting Model
Simulated
Flowchart
Forecasting Results and Statistical Analysis
Conclusions

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