Energy Consumption Forecasting using Machine Learning
Author(s):
Neenu Johnson, Shreya Cholera, Vibha Rajput
Keywords:
Abstract
Forecasting electricity demand and consumption accurately is critical to the optimal and cost effective operation system, providing a competitive advantage to companies. In working with seasonal data and external variables, the traditional time-series forecasting methods cannot be applied to electricity consumption data. In energy planning for a generating company, accurate power forecasting for the electrical consumption prediction, as a technique, to understand and predict the market electricity demand is of paramount importance. Their power production can be adjusted accordingly in a deregulated market. As data type is seasonal, Seasonal Autoregressive Integrated Moving Averages with exogenous repressors (SARIMAX), and Decision Tree, Random forest is used to explicitly deal with seasonality as a class of time-series forecasting models. The main purpose of this project is to perform exploratory data analysis of the Spain power, then use different forecasting models to once-daily predict the next 24 hours of energy demand and daily peak demand. To split the electricity consumption data into training and test sets. << The obtained results showed that the machine learning algorithms proposed in the recent literature outperformed the tested algorithms. Models are evaluated using root mean squared error (RMSE) to be directly comparable to energy readings in the data. RMSE has calculated two ways. First to represent the error of predicting each hour at a time. Second to represent the models’ overall performance. The results show that electricity demand can be modeled using machine learning algorithms, deploying renewable energy, planning for high/low load days, and reducing wastage from polluting on reserve standby generation, detecting abnormalities in consumption trends, and quantifying energy and cost-saving measures.
Article Details
Unique Paper ID: 158582

Publication Volume & Issue: Volume 9, Issue 10

Page(s): 46 - 49
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