Waste-to-energy (WtE) is national policy. From this view, WtE technology has been promoted. Many WtE projects in Thailand were unsuccessful due to several problems. This research aimed to analyze the key barriers impacting the WtE project development in Thailand. The Interpretive Structural Model (ISM) and Cross-Impact Matrix Multiplication Applied to Classification (MICMAC) analysis tool have been used to evaluate the barriers that significantly in the development of WtE projects. In this study, WtE projects focused on electricity power generation in order to correspond to the Alternative Energy Development Plan (AEDP) target and power purchase agreement constrain of the government. The barriers were obtained from six sections consisting of social issues, environment, national policy, technology, economy, and project management. From six sections, there are 20 barriers that were identified. The ISM and MICMAC analysis showed that the key barriers impacting the WtE projects development were insufficient amount of waste and poor waste management planning. These two barriers correspond with many studies in Thailand and other countries. The project developers or investors must take these two barriers and other barriers with less impact mentioned in this study into account before developing the WtE projects in Thailand.

The creation of significant power from PV technology has resulted from the growth of the solar PV business and industry and the lowering of PV costs. The electricity market is open broader for PV and other involved device technology. The goal of cumulative capacity drives Thailand's solar PV investment compass. The learning curve projection shows that the prediction's learning curve reliability can provide a clearer view for PV investment and policymakers. This paper shows the creation of the opportunity for the solar PV rooftop as a service (RaaS) business model, designed for four customers: residential and commercial, with small, medium, and large scales depending on their electricity consumption. The result reveals that if no grid is allowed, the medium- and large-scale are more likely to be feasible as they show grid parity. With the PV cost reduction trend, all the customers are economically viable in the grid sale allowed. Besides that, the electricity tariff rate from the grid also significantly impacts the PV rooftop investment and customer decision-making.

Electricity load forecasting is an important aspect of power system management. Improving forecasting accuracy ensures reliable electricity supply, grid operations, and cost savings. Often, collected data consist of Missing Values (MVs), anomalies, outliers, or other inconsistencies caused by power failures, metering errors, data collection errors, hardware failures, network failures, or other unexpected events. This study uses real-world data to investigate the possibility of using synthetically generated data as an alternative to filling in MVs. Three datasets were created from an original one based on different imputation methods. The imputation methods employed were linear interpolation, imputation using synthetic data, and a proposed hybrid method based on linear interpolation and synthetic data. The performance of the three datasets was compared using deep learning, machine learning, and statistical models and verified based on forecasting accuracy improvements. The findings demonstrate that the hybrid dataset outperformed the other interpolation methods based on the forecasting accuracy of the models.

The growing demand for electric vehicle charging stations is due to the increasing number of electric vehicles due to the rapid development of electric vehicle production and people buying more. This has also increased the demand for electric vehicle charging stations to travel between cities. This is because most combustion cars nowadays use fossil fuels. It harms clean air, producing carbon dioxide (CO2), PM2.5, and greenhouse gas emissions, resulting in climate changes and natural environmental impacts. Directly affect people in terms of health, living, and present life. By emphasizing the participation of business and government sectors, Thailand has encouraged investment in producing and importing electric vehicles to replace fossil fuel combustion. They also promoted the development of electric cars to be more efficient and run longer distances. The cumulative number of electric vehicles from 2017 to the present is increasing, making business opportunities for EV charging stations available in Thailand. From business model analysis was designed in 3 scenarios, resulting in Scenario1 Normal charger 6 outlets, in which the cost is low, the cost recovery time is the most, and it has a low net profit margin installed in shopping malls, restaurants, and residential condominiums. Scenario2 Quick charger 6 outlets take less time to charge. A high charging and increased investment cost have a short payback period installed in front of convenience stores and the current gas station. Scenario3 is a combination of an electric vehicle charging station with a normal charger and quick charger total of 2 outlets, installed in large areas such as current gas stations, logistics centers, and department stores with souvenir shops and convenience stores. This information can help analyze costs and plan investment decisions on the ownership of electric vehicle charging stations. In addition, electric vehicle charging station scenarios can be compared to predict the net profit margin of the business model.

Currently, the Thailand government is promoting biofuel, especially the producer of biodiesel. Starting from 2015, the Ministry of Energy of Thailand has determined that the palm oil remaining from domestic consumption is 14 million liters per day in 2036. Forecasting biodiesel prices are most important since biodiesel price volatility affects renewable energy consumption in the future. This paper presents the biodiesel prices in Thailand with the time series decomposition method. The source of time series data comes from the Energy Policy and Planning Office, Ministry of Energy of Thailand, monthly average retail price of regular-grade biodiesel, during 2007 – 2016, 120 months in total. This study aims to use forecasting methods to deter biodiesel prices in Thailand over the next 20 years, from 2017 to 2036. This solution starts with decomposing data into a trend, a cycle, seasonal, and any irregular components and then calculates biodiesel prices with a multiplicative model. The model shows a continuous decreasing trend of biodiesel prices from around 27.50 to 25.84 THB/liter in 2017 to 22.36 THB/liter in 2036. Moreover, the forecasting method has the least mean absolute present error (MAPE) at 0.24651. Keywords: Biodiesel, Forecasting, Time-Series Method JEL Classifications: C01, C10, E17, O21 DOI: https://doi.org/10.32479/ijeep.10666