Accurate electricity demand forecasting for a short horizon is very important for day-to-day control, scheduling, operation, planning, and stability of the power system. The main factors that affect the forecasting accuracy are deterministic variables and weather variables such as types of days and temperature. Due to the tropical climate of Thailand, the marginal impact of weather variables on electricity demand is worth analyzing. Therefore, this paper primarily focuses on the impact of temperature and other deterministic variables on Thai electricity demand. Accuracy improvement is also considered during model design. Based on the characteristics of demand, the overall dataset is divided into four different subgroups and models are developed for each subgroup. The regression models are estimated using Ordinary Least Square (OLS) methods for uncorrelated errors, and General Least Square (GLS) methods for correlated errors, respectively. While Feed Forward Artificial Neural Network (FF-ANN) as a simple Deep Neural Network (DNN) is estimated to compare the accuracy with regression methods, several experiments conducted for determination of training length, selection of variables, and the number of neurons show some major findings. The first finding is that regression methods can have better forecasting accuracy than FF-ANN for Thailand’s dataset. Unlike much existing literature, the temperature effect on Thai electricity demand is very interesting because of their linear relationship. The marginal impacts of temperature on electricity demand are also maximal at night hours. The maximum impact of temperature during night hours happens at 11 p.m., is 300 MW/ ° C, about 4 % rise in demand while during day hours, the temperature impact is only 10 MW/ ° C to 200 MW/ ° C about 1.4 % to 2.6 % rise.
Motivated by Japan's economic experiences and policy debates over the past two decades, this paper uses an open economy dynamic stochastic general equilibrium model to examine the volatility and welfare impact of alternative monetary policies. To capture the dynamic effects of likely structural breaks in the Japanese economy, we model agents' expectation formation process with an adaptive learning framework, and compare four Taylor-styled policy rules that reflect concerns commonly raised in Japan's actual monetary policy debate. We first show that imperfect knowledge and the associated learning process induce higher volatility in the economy, while retaining some of the policy conclusions from rational-expectations setups. In particular, explicit exchange rate stabilization is unwarranted, and under volatile foreign disturbances, policymakers should consider targeting domestic price inflation rather than consumer price inflation. However, contrary to results based on rational expectations, we show that even though highly inflation-sensitive rules do raise output volatility, they may nevertheless improve overall welfare in an adaptive learning setting by smoothing inflation fluctuations. Our findings suggest that previous policy conclusions that are based on partial equilibrium analyses, or that ignore likely deviations from rational expectations, may not be robust.
JEL classification: D84; E52; F41Keywords: Adaptive learning; Monetary policy rules; Open economy ____________________________ * We thank, without implicating, Drew Creal, George Evans, Seppo Honkapohja, Ben McCallum, Athanasios Orphanides, Richard Startz, George Waters, John Williams, Noah Williams, Wei-Choun Yu, and seminar participants at the Federal Reserve Bank of San Francisco and University of Washington for useful comments and suggestions. We also thank Arita Thatte for research assistance. Any remaining errors are our own.
Electricity demand forecasting plays a significant role in energy markets. Accurate prediction of electricity demand is the key factor to optimize power generation, consumption, saving energy resources, and determining the energy prices. However, integrating energy mix scenarios, including solar and wind power which are highly non-linear and seasonal, into an existing grid increases uncertainty in generation, adds the challenges for precise forecast. To tackle these challenges, state-of-the-art methods and algorithms have been implemented in literature. We have developed Artificial Intelligence (AI) based deep learning models that can effectively handle the information of long time-series data. Based on the pattern of dataset, four different scenarios were developed and two best scenarios were selected for prediction. Dozens of models were developed and tested in deep AI networks. In the first scenario (Scenario1), data for weekdays excluding holidays was taken and in the second scenario (Scenario2) all the data in the basket was taken. Remaining two scenarios, weekends and holidays were tested and neglected because of their high prediction error. To find the optimal configuration, models were trained and tested within a large space of alternatives called hyper-parameters. In this study, an Aritificial Neural Network (ANN) based Feed-forward Neural Network (FNN) showed the minimum prediction error for Scenario1 while a Recurrent Neural Network (RNN) based Gated Recurrent Network (GRU) showed the minimum prediction error for Scenario2. While comparing the accuracy, the lowest MAPE of 2.47% was obtained from FNN for Scenario1. When evaluating the same testing dataset (non-holidays) of Scenario2, the RNN-GRU model achieved the lowest MAPE of 2.71%. Therefore, we can conclude that grouping of weekdays as Senario1 prepared by excluding the holidays provides better forecasting accuracy compared to the single group approach used in Scenario2, where all the dataset is considered together. However, Scenario2 is equally important to predict the demand for weekends and holidays.
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