Abstract
Air Conditioners (AC) impact in overall electricity consumption in buildings is very high. Therefore, controlling ACs power consumption is a significant factor for demand response. With the advancement in the area of demand side management techniques implementation and smart grid, precise AC load forecasting for electrical utilities and end-users is required. In this paper, big data analysis and its applications in power systems is introduced. After this, various load forecasting categories and various techniques applied for load forecasting in context of big data analysis in power systems have been explored. Then, Levenberg–Marquardt Algorithm (LMA)-based Artificial Neural Network (ANN) for residential AC short-term load forecasting is presented. This forecasting approach utilizes past hourly temperature observations and AC load as input variables for assessment. Different performance assessment indices have also been investigated. Error formulations have shown that LMA-based ANN presents better results in comparison to Scaled Conjugate Gradient (SCG) and statistical regression approach. Furthermore, information of AC load is obtainable for different time horizons like weekly, hourly, and monthly bases due to better prediction accuracy of LMA-based ANN, which is helpful for efficient demand response (DR) implementation.
Highlights
In power systems, the end-users electrical demand characteristics have the most significant role
The main contributions of this manuscript are as follows: 1) Big data analysis and its applications in power systems are discussed, and main applications of Load Forecasting (LF) approaches of Air Conditioners (AC) demand have been highlighted for demand response (DR) accomplishment; 2) With weather information and past load data considered, a novel Levenberg–Marquardt Algorithm (LMA)-based Artificial Neural Network (ANN) approach to forecast future residential AC loads is proposed, which is suitable for different time horizons like weekly, hourly and monthly basis, and could improve the accuracy of forecasting; 3) Different performance assessment indices are presented and real time hourly TMY3 data for Austin Texas is used for demonstrating the proposed LMA-based ANN approach, which show that the proposed LMA-based ANN approach is better than Scaled Conjugate Gradient (SCG)-based ANN and conventional multiple linear regression approach
It has been investigated that AC impact in overall electricity consumption in buildings is very high
Summary
The end-users electrical demand characteristics have the most significant role. The main contributions of this manuscript are as follows: 1) Big data analysis and its applications in power systems are discussed, and main applications of LF approaches of AC demand have been highlighted for DR accomplishment; 2) With weather information and past load data considered, a novel LMA-based ANN approach to forecast future residential AC loads is proposed, which is suitable for different time horizons like weekly, hourly and monthly basis, and could improve the accuracy of forecasting; 3) Different performance assessment indices are presented and real time hourly TMY3 data for Austin Texas is used for demonstrating the proposed LMA-based ANN approach, which show that the proposed LMA-based ANN approach is better than SCG-based ANN and conventional multiple linear regression approach.
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