Real-time day ahead energy management for smart home using machine learning algorithm

Issue title: Soft Computing and Intelligent Systems: Techniques and Applications

Guest editors: Sabu M. Thampi, El-Sayed M. El-Alfy and Ljiljana Trajkovic

Article type: Research Article

Authors: Vasudevan, Nisha^{a; *} | Venkatraman, Vasudevan^b | Ramkumar, A^a | Sheela, A^c

Affiliations: [a] Department of Electrical and Electronics Engineering, Kalasalingam Academy of Research and Education, Krishnankoil, Virudhunagar District, Tamilnadu, India | [b] Department of Computer Science Engineering, Kalasalingam Academy of Research and Education, Krishnankoil, Virudhunagar District, Tamilnadu, India | [c] Department of Electrical and Electronics Engineering, Kongu Engineering College, Perundurai

Correspondence: [*] Corresponding author. Nisha Vasudevan, Department of Electrical and Electronics Engineering, Kalasalingam Academy of Research and Education, Krishnankoil, Virudhunagar District, Tamilnadu, India. Tel.: +91 9884272554; E-mail: [email protected].

Abstract: Smart grid is a sophisticated and smart electrical power transmission and distribution network, and it uses advanced information, interaction and control technologies to build up the economy, effectiveness, efficiency and grid security. The accuracy of day-to-day power consumption forecasting models has an important impact on several decisions making, such as fuel purchase scheduling, system security assessment, economic capacity generation scheduling and energy transaction planning. The techniques used for improving the load forecasting accuracy differ in the mathematical formulation as well as the features used in each formulation. Power utilization of the housing sector is an essential component of the overall electricity demand. An accurate forecast of energy consumption in the housing sector is quite relevant in this context. The recent adoption of smart meters makes it easier to access electricity readings at very precise resolutions; this source of available data can, therefore, be used to build predictive models., In this study, the authors have proposed Prophet Forecasting Model (PFM) for the application of forecasting day-ahead power consumption in association with the real-time power consumption time series dataset of a single house connected with smart grid near Paris, France. PFM is a special type of Generalized Additive Model. In this method, the time series power consumption dataset has three components, such as Trend, Seasonal and Holidays. Trend component was modelled by a saturating growth model and a piecewise linear model. Multi seasonal periods and Holidays were modelled with Fourier series. The Power consumption forecasting was done with Autoregressive Integrated Moving Average (ARIMA), Long Short Term Neural Memory Network (LSTM) and PFM. As per the comparison, the improved RMSE, MSE, MAE and RMSLE values of PFM were 0.2395, 0.0574, 0.1848 and 0.2395 respectively. From the comparison results of this study, the proposed method claims that the PFM is better than the other two models in prediction, and the LSTM is in the next position with less error.

Keywords: Energy management, smart home, energy forecast, power management, efficiency

DOI: 10.3233/JIFS-189886

Journal: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 5, pp. 5665-5676, 2021