Performance Evaluation of Two Machine Learning Techniques in Heating and Cooling Loads Forecasting of Residential Buildings

Abstract

Nowadays, since energy management of buildings contributes to the operation cost, many efforts are made to optimize the energy consumption of buildings. In addition, the most consumed energy in the buildings is assigned to the indoor heating and cooling comforts. In this regard, this paper proposes a heating and cooling load forecasting methodology, which by taking this methodology into the account energy consumption of the buildings can be optimized. Multilayer perceptron (MLP) and support vector regression (SVR) for the heating and cooling load forecasting of residential buildings are employed. MLP and SVR are the applications of artificial neural networks and machine learning, respectively. These methods commonly are used for modeling and regression and produce a linear mapping between input and output variables. Proposed methods are taught using training data pertaining to the characteristics of each sample in the dataset. To apply the proposed methods, a simulated dataset will be used, in which the technical parameters of the building are used as input variables and heating and cooling loads are selected as output variables for each network. Finally, the simulation and numerical results illustrates the effectiveness of the proposed methodologies.

Short-Term Load Forecasting of Microgrid via Hybrid Support Vector Regression and Long Short-Term Memory Algorithms

“…The principle advantage of SVR is to solve regression issues and forecast future values. Among the various versions of the SVR, the classic model (ε-SVR) that is mainly used in engineering and also employed in this paper [10,49]. In ε-SVR, the goal is finding a flat function, which maps the input data to output data with an error less than ε.…”

“…To deal with the infeasible constraints of the optimization issue in Equation 2, slack variables, i.e., ξ i and ξ * i , can be presented. Hence, Equation (2) can be restated as [10]:…”

“…where i shows the number of input data, N is the number of the last instance of the input data, c > 0 is a trade-off among the smoothness of f (t) and the permissible deviation greater than ε. To extend the formulation for nonlinear functions, the dual problem of Equation (3) can be derived using Lagrangian multipliers, i.e., α i , α * i , η i , and η * i , forming the Lagrange function as follows [10]:…”

“…In this paper, statistical performance metrics, such as correlation coefficient (R), Mean Square Error (MSE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE) are introduced and utilized to assess the results. Those metrics are calculated as follows [10]:…”

Machine Learning Based PEVs Load Extraction and Analysis

“…Therefore, the highest value of R to 1 and the lowest values for MSE and RMSE to zero indicate the best network performance. Each of these statistical performance criteria can be calculated as follows [37,38]:…”