Smart Solar Home System with Solar Forecasting

Renewable energy management in smart home environment via forecast embedded scheduling based on Recurrent Trend Predictive Neural Network

“…In [32], Shakir et al developed a home energy management system using LSTM for forecasting and Genetic Algorithm for optimization. In [33], Manue et al used LSTM to forecast the load for battery utilization in a solar system in a smart home system. In [34] developed a hybrid system of renewable and grid-supplied energy via exponential weighted moving average-based forecasting and a heuristic load control algorithm.…”

“…We now compare the forecasting performance of rTPNN with the performances of LSTM, MLP, Linear Regression, Lasso, Ridge, ElasticNet, Random Forest as well as 1-Day Naive Forecast. 1 Recall that in recent literature, References [31,32,33] used LSTM, and Reference [14,15,19,38]…”

Forecasting Solar Home System Customers’ Electricity Usage with a 3D Convolutional Neural Network to Improve Energy Access

“…These factors influence the ability to forecast electricity consumption of off-grid users in developing countries. Electricity usage of SHSs can also be highly variable due to the intermittency of the energy supply and battery capacity limitations that can result in individuals running out of battery [8,12]. Another factor that contributes to this intermittency is the dominance of PAYG payments that enable individuals to only pay for electricity usage when they can afford to.…”

“…Much of the existing literature focusses on developed nations, which operate in a different context, usually centred on electricity grid consumers. The SHS consumption forecasting sector is particularly nascent, where a study by Manur et al [12] was the only paper discovered to tackle this issue. They used an LSTM to predict the next hour's usage of a single SHS customer in India.…”

“…The SHS provider can use these predictions to better cater to their customers' needs based on their specific past and future usage profile. For instance, a household's forecast may reveal that their consumption levels will result in them regularly running out of battery, which other studies have also identified as an issue affecting consumers [8,12]. The company could then intervene by giving customers the option to switch to a SHS with a higher capacity that would match their requirements.…”