{"id":48419,"date":"2023-09-26T10:45:04","date_gmt":"2023-09-26T08:45:04","guid":{"rendered":"https:\/\/www.inovex.de\/?p=48419"},"modified":"2023-09-26T10:45:04","modified_gmt":"2023-09-26T08:45:04","slug":"voltage-and-variables-exploring-forecasting-for-electricity-consumption-through-time-series-analysis-with-the-prophet-model","status":"publish","type":"post","link":"https:\/\/www.inovex.de\/de\/blog\/voltage-and-variables-exploring-forecasting-for-electricity-consumption-through-time-series-analysis-with-the-prophet-model\/","title":{"rendered":"Voltage and Variables \u2013 Exploring Forecasting for Electricity Consumption through Time Series Analysis with the Prophet Model"},"content":{"rendered":"

Accurate forecasting of electricity consumption is greatly beneficial to companies aiming to achieve efficient and optimized cost management. The key lies in understanding and interpreting respective time series data \u2013 a collection of energy consumption measurements recorded at evenly spaced intervals. Analyses and forecasting of time series offer valuable insights into past patterns, trends, periodic fluctuations, and future developments of electricity usage. These insights empower informed decisions regarding resource management.<\/p>\n

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In this context, statistical and algorithmic approaches are used to ground forecasts on historical data. In recent years, numerous forecasting models have been developed, each leveraging distinct assumptions about the underlying patterns in time series data. Depending on these patterns, the resultant models are suitable for specific applications.<\/p>\n

In this blog post, we will take a closer look at a modular regression model called Prophet as a promising model for time series analysis. The investigated time series of energy consumption shows regular seasonalities, and huge trend changes due to external factors or holiday effects. Throughout this blog post, we will detail the process of applying the Prophet model to such data and ascertain its efficacy.<\/p>\n

The objective is to develop an implementation that can be applied to a wide range of different energy consumption data. Thus, we will also emphasize an evaluation strategy that is helpful to evaluate multiple time series. This approach facilitates the development of promising performance conditions for Prophet.<\/p>\n

Disclaimer<\/em>:
\nThe content of the blog post assumes familiarity with machine learning concepts, and readers are advised to have a solid understanding of time series concepts for optimal comprehension.<\/p>\n

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