Abstract
This study explores investigation of applicability of impact factors to estimate solar irradiance by four machine learning algorithms using climatic elements as comparative analysis: linear regression, support vector machines (SVM), a multi-layer neural network (MLNN), and a long short-term memory (LSTM) neural network. The methods show how actual climate factors impact on solar irradiation, and the possibility of estimating one year local solar irradiance using machine learning methodologies with four different algorithms. This study conducted readily accessible local weather data including temperature, wind velocity and direction, air pressure, the amount of total cloud cover, the amount of middle and low-layer cloud cover, and humidity. The results show that the artificial neural network (ANN) models provided more close information on solar irradiance than the conventional techniques (linear regression and SVM). Between the two ANN models, the LSTM model achieved better performance, improving accuracy by 31.7% compared to the MLNN model. Impact factor analysis also revealed that temperature and the amount of total cloud cover are the dominant factors affecting solar irradiance, and the amount of middle and low-layer cloud cover is also an important factor. The results from this work demonstrate that ANN models, especially ones based on LSTM, can provide accurate information of local solar irradiance using weather data without installing and maintaining on-site solar irradiance sensors.
Highlights
Information on solar irradiance and activity is one of the major challenges facing the efficient use of solar power, because solar irradiance has a significant impact on theEarth’s energy system [1]
This study explored deduction strategies for solar irradiance and sensitivity analysis based on actual local climatic parameters using four prediction algorithms: linear regression, support vector machines (SVM), a multi-layer neural network (MLNN), and a long short-term memory (LSTM) network
The strategies were based on four machine learning algorithms: linear regression, SVM, MLNN, and LSTM
Summary
Information on solar irradiance and activity is one of the major challenges facing the efficient use of solar power, because solar irradiance has a significant impact on theEarth’s energy system [1]. Besides the 7.8% of final consumption from traditional biomass, 10.4% of total energy consumption comes from modern renewables consisting of wind, solar, biofuels, ocean power, and others. From the viewpoint of investment, solar energy generation accounted for more than 55% of all newly installed renewable power capacity in 2017, with wind accounting for 29% [2]. Solar energy generation and building energy consumption have been increasing, especially in China [3,4]. Effective solar energy generation has become a very important renewable energy source, and the energy provided by increased solar panel installations in buildings can meet this increasing demand for energy, together with traditional fossil energy sources [5,6]. Electricity energy generated from solar panels is quite reliant on local solar irradiance [7]
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