{'fraction_non_alphanumeric': 0.017817371937639197, 'fraction_numerical': 0.0014847809948032665, 'mean_word_length': 5.51207729468599, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Performance Analysis of Short-term Electricity Demand Forecasting for Thailand

Accurate electricity demand forecasting for a short horizon is very important for day to day operation, planing to ensure the stability of electric system. Deterministic and weather variables are the crucial external factors that effect electricity demand. Many researchers define temperature as the major weather component because other weather components such as: relative humidity, wind chill, and solar radiation have less effect if temperature is incorporated in a model. Two parsimonious models (Model A and Model B) are developed and estimated using feed forward back propagation algorithm and Bayesian technique. Model A consists of historical demand data with deterministic terms, where as Model B consist of all variables of Model A and temperature variables. The result shows that the marginal impacts of temperature have different effects in different years. Occurrence of holidays uplifted the overall forecasting error. Multiple linear regression (MLR) modeling approach shows better accuracy than ANN approach. In overall, temperature variable can improve the forecasting accuracy at least by 19%. This increment in accuracy is noticeable for all individual hours. However, accuracy improvement is higher in morning comparing to evening than day hours.