Wutthipum Kanchana

Bio: Wutthipum Kanchana is an academic researcher from Kasetsart University. The author has contributed to research in topics: Renewable energy & Electric power. The author has an hindex of 1, co-authored 1 publications receiving 1 citations.

PV Power Forecasting with Holt-Winters Method

Abstract: Due to global warming issue and growing of energy demand, photovoltaic (PV) power plant is a desirable alternative to electrical power generations. An accurate PV power forecasting is essential to alleviate its negative impacts and to improve its performance. This paper proposed a short-term PV power forecasting technique based on Holt-Winters method. Its property and principle are presented. There are two main parameters, i.e. the number of days (N) and the weight index (α). The values of these two parameters affect the forecasting accuracy, and a set of optimal values for both parameters used for all season is proposed. A real PV generated power profile is collected from the eastern part of Thailand and used in this study. The performance of the proposed forecasting technique is carried out by MATLAB program. The simulation results indicate that the proposed method provides acceptable short-term forecasting results. The proposed technique can be applied in energy management and electrical power smoothing applications.

Detection of Invisible Solar PV Installations in Power Distribution System: A Review

Abstract: Climate change has squeezed people to have massive coordination in carbon neutrality. Clean energy must prevail over power demand to achieve our goal. The net zero carbon emission roadmap encourages the energy sector to produce energy from alternative energy resources. Solar Photovoltaic (PV) systems, a distributed energy resource, are a promising solution to produce power with less carbon emissions. However, the high adoption of solar PV systems can cause negative impacts on distribution networks. The network operators should realize the capacity of distributed energy resources in their systems to maintain power supply and perform active local energy management. This study aims to fill the gap in the detection of invisible solar PV installations. In this paper, proposed PV identification and PV capacity estimation techniques are investigated. The processes of study are determined and summarized. The coming steps of this research area will be discussed in possible ways.

Short-term forecasting of photovoltaic solar power production using variational auto-encoder driven deep learning approach

Abstract: The accurate modeling and forecasting of the power output of photovoltaic (PV) systems are critical to efficiently managing their integration in smart grids, delivery, and storage. This paper intends to provide efficient short-term forecasting of solar power production using Variational AutoEncoder (VAE) model. Adopting the VAE-driven deep learning model is expected to improve forecasting accuracy because of its suitable performance in time-series modeling and flexible nonlinear approximation. Both single- and multi-step-ahead forecasts are investigated in this work. Data from two grid-connected plants (a 243 kW parking lot canopy array in the US and a 9 MW PV system in Algeria) are employed to show the investigated deep learning models’ performance. Specifically, the forecasting outputs of the proposed VAE-based forecasting method have been compared with seven deep learning methods, namely recurrent neural network, Long short-term memory (LSTM), Bidirectional LSTM, Convolutional LSTM network, Gated recurrent units, stacked autoencoder, and restricted Boltzmann machine, and two commonly used machine learning methods, namely logistic regression and support vector regression. The results of this investigation demonstrate the satisfying performance of deep learning techniques to forecast solar power and point out that the VAE consistently performed better than the other methods. Also, results confirmed the superior performance of deep learning models compared to the two considered baseline machine learning models.

Short-Term forecasting of photovoltaic power in an isolated area of Ecuador using deep learning techniques

Abstract: In Ecuador, electricity generation is mainly covered by renewable energy sources that feed the National Interconnected System (NIS). Its economical price means that the installation of systems based on non-conventional renewable energy sources does not represent a benefit for the user. However, rural communities isolated from the NIS do not have electricity services. Due to this, isolated systems become an effective option to supply electricity to these communities. In this regard, the Galapagos Islands have unique biodiversity in the world. Since they are not connected to the NIS, their primary energy sources are based on biogas obtained from fossil fuels, with their negative consequences despite the great potential of solar resources. Hence the need to use non-conventional renewable energy sources that covers the energy demand and do not affect the biodiversity there. Photovoltaic energy forecasting is an essential step in the installation of photovoltaic systems. Prediction models based on deep learning (DL) techniques can obtain a high degree of accuracy in energy prediction tasks. For this reason, this work presents the development of long short-term memory (LSTM) and gated recurrent unit (GRU) models to predict photovoltaic energy in an isolated area of Ecuador. The results highlight the performance of both methods through the achieved short-term prediction.

Forecast of photovoltaic generation in isolated rural areas of Ecuador using Holt-Winters and seasonal variation methods

Abstract: Due to the shortage of electric power in isolated rural areas of Ecuador, implementing a photovoltaic power generation system is an optimal, viable, and sustainable alternative that can reduce the gap in electric power coverage in the nation. However, since the geographical location of a possible PV system implementation directly affects its optimal performance, it is essential to propose generation forecasting algorithms that use actual data from the place under study. For this reason, this paper compares Holt-Winters and Seasonal Variation forecasting methods applied to two rural locations in Ecuador. These methods use historical records of meteorological conditions such as solar irradiation and ambient temperature to achieve a short-term forecast of photovoltaic generation. Percentage errors obtained in the forecast power and the Pearson correlation coefficient are calculated as performance measures. The results show that although both algorithms have similar characteristics when getting the photovoltaic generation prediction, the Holt-Winters method presents improved behavior.

Indonesian Journal of Electrical Engineering and Computer Science

Abstract: Received Jun 3, 2022 Revised Nov 4, 2022 Accepted Nov 10, 2022 The prediction of performance of photovoltaic technologies is crucial, not only to improve the reliability and durability of these technologies but also to increase the confidence of investors and consumers in them. The accurate calculation of the degradation rate DR (%) in real operating conditions under specific climatic stresses is, therefore, paramount. The present study provides a comparison of performance losses of two silicon PV technologies installed on the rooftop of the Higher School of Technology in Laâyoune-Morocco. The two systems are a polycristalline array (pc-Si: 1.82 kWp) and an amorphous array (a-Si: 1.55 KWp), which are grid connected. In the light of related performance gathered over three-year, the degradation rates of the two systems were estimated using four statistical methods under the open-source software R. The techniques engaged in this paper are: classical seasonal decomposition (CSD), holt-winters (HW), autoregressive integrated moving average (ARIMA), and seasonal and trend decomposition by LOESS (STL). The results obtained using those methods show that DR(%) varies between 0.39% and 0.99% for pc-Si and between 0.29% and 0.64% for a-Si. The analysis of degradation accuracy shows that STL and CSD techniques provide results with high accuracy than ARIMA and HW for the two systems. The present study adds to knowledge on PV degradation under the subtropical desert climate of Laâyoune.