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

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.

Review on the Application Areas of Decision-Making Algorithms in Smart Homes

“…Tao et al [39] provide an efficient deep learning framework to predict the future energy consumption and also provide a communication between energy distributors and consumers. Accurate demand forecasting [40] is important for future strategic planning and scheduling. It can also help the consumers to minimize the cost of electricity.…”

Integrated Energy Management of Smart Grids in Smart Cities Based on PSO Scheduling Models

“…Currently, there is a lot of attention on the energy optimization management of CCHP-type microgrids. Vasudevan et al [12] introduced in detail the model of each unit of CCHP-type microgrid, planning methods, system evaluation indexes, and energy optimization management methods. Xie et al [13] proposed a new solution method based on the vertical and horizontal crossover algorithm that is proposed to solve the problem of economic scheduling optimization of cogeneration.…”