Monthly Electric Energy Demand Forecasting by Fuzzy Inference System

2016 ◽  
Author(s):  
Ivette Luna ◽  
Rosangela Ballini
Keyword(s):  
Fuzzy Inference System ◽  
Energy Demand ◽  
Fuzzy Inference ◽  
Demand Forecasting ◽  
Electric Energy ◽  
Inference System ◽  
Energy Demand Forecasting

scholarly journals Usage of the Pareto Fronts as a Tool to Select Data in the Forecasting Process—A Short-Term Electric Energy Demand Forecasting Case

Energies ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 3204
Author(s):  
Michał Sabat ◽  
Dariusz Baczyński
Keyword(s):  
Energy Demand ◽  
Demand Forecasting ◽  
Electric Energy ◽  
Forecasting Model ◽  
Short Term ◽  
Complex Evaluation ◽  
Proposed Model ◽  
Energy Demand Forecasting ◽  
Pareto Fronts ◽  
Naive Approach

Transmission, distribution, and micro-grid system operators are struggling with the increasing number of renewables and the changing nature of energy demand. This necessitates the use of prognostic methods based on ever shorter time series. This study depicted an attempt to develop an appropriate method by introducing a novel forecasting model based on the idea to use the Pareto fronts as a tool to select data in the forecasting process. The proposed model was implemented to forecast short-term electric energy demand in Poland using historical hourly demand values from Polish TSO. The study rather intended on implementing the range of different approaches—scenarios of Pareto fronts usage than on a complex evaluation of the obtained results. However, performance of proposed models was compared with a few benchmark forecasting models, including naïve approach, SARIMAX, kNN, and regression. For two scenarios, it has outperformed all other models by minimum 7.7%.

scholarly journals Enhancing Historic Building Performance with the Use of Fuzzy Inference System to Control the Electric Cooling System

2020 ◽  
Vol 12 (14) ◽  
pp. 5848
Author(s):  
Antonio Martinez-Molina ◽  
Miltiadis Alamaniotis
Keyword(s):  
Energy Consumption ◽  
Relative Humidity ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Cooling System ◽  
Electric Energy ◽  
Operation Time ◽  
Building Performance ◽  
Inference System ◽  
Historic Building

In recent years, the interest in properly conditioning the indoor environment of historic buildings has increased significantly. However, maintaining a suitable environment for building and artwork preservation while keeping comfortable conditions for occupants is a very challenging and multi-layered job that might require a considerable increase in energy consumption. Most historic structures use traditional on/off heating, ventilation, and air conditioning (HVAC) system controllers with predetermined setpoints. However, these controllers neglect the building sensitivity to occupancy and relative humidity changes. Thus, sophisticated controllers are needed to enhance historic building performance to reduce electric energy consumption and increase sustainability while maintaining the building historic values. This study presents an electric cooling air controller based on a fuzzy inference system (FIS) model to, simultaneously, control air temperature and relative humidity, taking into account building occupancy patterns. The FIS numerically expresses variables via predetermined fuzzy sets and their correlation via 27 fuzzy rules. This intelligent model is compared to the typical thermostat on/off baseline control to evaluate conditions of cooling supply during cooling season. The comparative analysis shows a FIS controller enhancing building performance by improving thermal comfort and optimizing indoor environmental conditions for building and artwork preservation, while reducing the HVAC operation time by 5.7%.

Monthly electric energy demand forecasting with neural networks and Fourier series

2008 ◽  
Vol 49 (11) ◽  
pp. 3135-3142 ◽  
Author(s):  
E. González-Romera ◽  
M.A. Jaramillo-Morán ◽  
D. Carmona-Fernández
Keyword(s):  
Neural Networks ◽  
Fourier Series ◽  
Energy Demand ◽  
Demand Forecasting ◽  
Electric Energy ◽  
Energy Demand Forecasting

Monthly Electric Energy Demand Forecasting Based on Trend Extraction

2006 ◽  
Vol 21 (4) ◽  
pp. 1946-1953 ◽  
Author(s):  
E. Gonzalez-Romera ◽  
M.A. Jaramillo-Moran ◽  
D. Carmona-Fernandez
Keyword(s):  
Energy Demand ◽  
Demand Forecasting ◽  
Electric Energy ◽  
Trend Extraction ◽  
Energy Demand Forecasting
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