Forecasting Solar Energy Generation and Household Energy Usage for Efficient Utilisation

Aistis Raudys () and Julius Gaidukevičius
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Aistis Raudys: Institute of Computer Science, Vilnius University, Didlaukio g. 47, LT-08303 Vilnius, Lithuania
Julius Gaidukevičius: Institute of Computer Science, Vilnius University, Didlaukio g. 47, LT-08303 Vilnius, Lithuania

Energies, 2024, vol. 17, issue 5, 1-33

Abstract: In this study, a prototype was developed for the effective utilisation of a domestic solar power plant. The basic idea is to switch on certain electrical appliances when the surplus of generated energy is predicted one hour in advance, for example, switching on a pump motor for watering a garden. This prediction is important because some devices (motors) wear out if they are switched on and off too frequently. If a solar power plant generates more energy than a household can consume, the surplus energy is fed into the main grid for storage. If a household has an energy shortage, the same energy is bought back at a higher price. In this study, data were collected from solar inverters, historical weather APIs and smart energy meters. This study describes the data preparation process and feature engineering that will later be used to create forecasting models. This study consists of two forecasting models: solar energy generation and household electricity consumption. Both types of model were tested using Facebook Prophet and different neural network architectures: feedforward, long short-term memory (LSTM) and gated recurrent unit (GRU) networks. In addition, a baseline model was developed to compare the prediction accuracy.

Keywords: neural networks; feedforward; long short-term memory; recurrent neural networks; LSTM; GRU; photovoltaic; solar (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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