Implementasi Support Vector Regression pada Prediksi Inflasi Indeks Harga Konsumen

Inflation reflects an increase in the prices of these items as well as those used by the Indonesian government, especially Bank Indonesia, in determining monetary policy. An indicator that can be obtained by Bank Indonesia in measuring inflation is the Consumer Price Index. This study discusses inflation prediction using the SVR method. Inflation test data issued by Bank Indonesia. As a comparison material for the kernel used in the SVR method using two kernels, namely Linear and Radial Base Function. The error rate evaluation results show that linear kernels produce better values, with a MAPE rate of 8.70% and MSE of 0.003

Electricity Consumption Classification using Various Machine Learning Models

INTRODUCTION: As population has increased over successive generations, human dependency on electricity has increased to the point where it has become a norm and indispensable, and the idea of living without it has become unthinkable. OBJECTIVES: Machine learning is emerging as a fundamental method for performing tasks autonomously without human intervention. Forecasting electricity consumption is challenging due to the many factors that influence it; embracing modern technology with its heavy focus on machine learning and artificial intelligence is a potential solution. METHODS: This study employs various machine learning algorithms to forecast power usage and determine which method performs best in predicting the dataset based on different variables. RESULTS: Eight models were tested, including Linear Regression, DT Classifier, RF Classifier, KNN, DT Regression, SVM, Logistic Regression, and GNB Classifier. The Decision Tree model had the greatest accuracy of 98.3%. CONCLUSION: The Decision Tree model’s accuracy can facilitate efficient use of electricity, leading to both conservation of electricity and cost savings, and be a guiding light in future planning

Support Vector Regression for Electricity Consumption Prediction in a Building in Japan

International audienceThis work studies how to apply support vector machines in order to forecast the energy consumption of buildings. Usually, support vector regression is implemented using the sequential minimal optimisation algorithm. In this work, an alternative version of that algorithm is used to reduce the execution time. Several experiments were carried out taking into account data measured during one year. The weather conditions were used as independent variables and the consumed amount of electricity was considered as the parameter to predict. The model has been trained using the first six months of the dataset whereas it was validated using the following three months and tested taking into account the last three months of measurements. From obtained results, a good performance of the model is observed. © 2016 IEEE