Abstract
Energy consumption forecasting is crucial in current and future power and energy systems. With the increasing penetration of renewable energy sources, with high associated uncertainty due to the dependence on natural conditions (such as wind speed or solar intensity), the need to balance the fluctuation of generation with the flexibility from the consumer side increases considerably. In this way, significant work has been done on the development of energy consumption forecasting methods, able to deal with different forecasting circumstances, e.g., the prediction time horizon, the available data, the frequency of data, or even the quality of data measurements. The main conclusion is that different methods are more suitable for different prediction circumstances, and no method can outperform all others in all situations (no-free-lunch theorem). This paper proposes a novel application, developed in the scope of the SIMOCE project (ANI|P2020 17690), which brings together several of the most relevant forecasting methods in this domain, namely artificial neural networks, support vector machines, and several methods based on fuzzy rule-based systems, with the objective of providing decision support for energy consumption forecasting, regardless of the prediction conditions. For this, the application also includes several data management strategies that enable training of the forecasting methods depending on the available data. Results show that by this application, users are endowed with the means to automatically refine and train different forecasting methods for energy consumption prediction. These methods show different performance levels depending on the prediction conditions, hence, using the proposed approach, users always have access to the most adequate methods in each situation.
Highlights
In recent decades, researchers have studied the problem of short-term forecasting energy consumption and its role in energy control systems [1]
We present a case study in order to evaluate the performance of the proposed application, This section presents a case study in order to evaluate the performance of the proposed applicaby assessing the accuracy of the proposed forecasting methods namely Artificial Neural Networks (ANN) [16], Support Vector Machines (SVMs) [40], Hybrid neural Fuzzy Inference System (HyFIS) [31], tion, by assessing the accuracy of the proposed forecasting methods namely ANN [16], SVM [40], Wang and Mendel’s method (WM) [33], GFS.FR.MOGUL [36] and verify the influence of using different training strategies on
It is not possible to conclude that there is an absolute forecasting strategy that has the best result for all thepaper forecasting methods
Summary
Researchers have studied the problem of short-term forecasting energy consumption and its role in energy control systems [1]. Many different studies have been published which propose various forecasting algorithms and models to predict the value of energy consumption This approach can be categorized into statistical, engineering, and artificial intelligence models [4]. By being developed as a domain-agnostic application, it can be applied and used in multiple other application domains for forecasting purposes, needing access to the log of historic data
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