Design of Ensemble Forecasting Models for Home Energy Management Systems
Abstract
:1. Introduction
1.1. Background Information
1.2. Objectives, Contributions, and Work Organization
- (i)
- A detailed review of ML techniques in energy forecasting in buildings and HEMS;
- (ii)
- A simple scheme to design ensemble models for forecasting the energy produced and consumed in residences with PV generation and battery storage. Notice that as PV generation forecasting also implies the forecast of solar irradiance and atmospheric temperature, four different forecasting models are needed for a HEMS.
2. Literature Review
2.1. Machine Learning (ML)-Based Prediction Methods for Energy Systems
2.2. Forecasting of Energy Consumption in Buildings
2.3. Applications of ML-Based Energy Systems Forecasting in HEMS
2.4. Future Applications for Schedulable and Non-Schedulable Appliance Consumption Forecasting Using NILM
3. Design Methodology
3.1. The Models
3.2. Model Design
- (i)
- Using the available data, training, generalization or testing, and validation sets should be constructed. This phase is known as data selection.
- (ii)
- Once datasets have been built, the structure of the models, as well as their inputs, should be determined. This phase is known as structure selection.
- (iii)
- For each model determined in the previous step, its parameters should be estimated. This is the estimation step.
3.2.1. Data Selection
3.2.2. Structure Selection
3.2.3. Parameter Estimation
3.3. Model Ensemble
4. Case Study Description
5. Results
5.1. Data Sets Description
5.2. Approxhull Results
5.3. MOGA Results
- Prediction Horizon: 28 steps (7 h);
- Number of neuros: ;
- Initial parameter values: OAKM [97];
- Number of training trials: five, best compromise solution;
- Termination criterion: early stopping, with a maximum number of iterations of 50;
- Number of generations: 100;
- Population size: 100;
- Proportion of random emigrants: 0.10;
- Crossover rate: 0.70.
5.3.1. Single Solution
Model 1—Power Demand
- ;
- ;
- OM < 150;
- Minimize .
Model 2—Solar Irradiance
- ;
- ;
- OM < 150;
- Minimize .
Model 3—Atmospheric Temperature
- ;
- ;
- OM < 100;
- Minimize .
Model 4—Power Generated
- OM < 100
- Minimize .
5.3.2. Ensemble Averaging
Model 1—Load Demand
Model 2—Solar Irradiance
Model 3—Atmospheric Temperature
Model 4—Power Generated
5.4. Discussion of the Results
Comparison of Results
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Conflicts of Interest
Abbreviations
ANN | Artificial Neural Networks |
ARIMA | Autoregressive Integrated Moving Average |
BAB | Branch and Bound |
BPS | Building Performance Simulation |
CH | Convex Hull |
DC | Direct Current |
ELM | Extreme Learning Machines |
EMS | Energy Management Systems |
GPR | Ground Penetrating Radar |
HEMS | Home Energy Management Systems |
HVAC | Heat Ventilation Air Conditioning |
HW | Holt-Winters |
k-NN | k-Nearest Neighbors |
LR | Linear Regression |
LSTM | Long Short-Term Memory |
ML | Machine Learning |
MOGA | Multi-Objective Genetic Algorithm |
NAR | Nonlinear Autoregressive |
NARX | Nonlinear Autoregressive Exogenous |
NILM | Non-Intrusive Load Monitoring |
NSGA-II | Nondominated Sorting Genetic Algorithm-II |
OAKM | Optimal Adaptative K-Means |
PH | Prediction Horizon |
PV | Photovoltaics |
RBF | Radial Basis Function |
RMSE | Root Mean Square Error |
RNN | Recursive Neural Networks |
SPWS | Self-Powered Wireless Sensors |
SVM | Support Vector Machine |
SVR | Support Vector Regression |
WBs | Wibees |
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Day of the Week | Regular Day | Holiday | Special |
---|---|---|---|
Monday | 0.05 | 0.40 | 0.70 |
Tuesday | 0.10 | 0.80 | |
Wednesday | 0.15 | 0.50 | |
Thursday | 0.20 | 1.00 | |
Friday | 0.25 | 0.60 | 0.90 |
Saturday | 0.30 | 0.30 | |
Sunday | 0.35 | 0.35 |
Execution | ||||
---|---|---|---|---|
1st | 0.14 | 0.12 | 0.12 | 4.81 |
2nd | 0.15 | 0.12 | 0.12 | 4.79 |
Execution | ||||
---|---|---|---|---|
1st | 0.11 | 0.08 | 0.08 | 2.58 |
2nd | 0.11 | 0.08 | 0.08 | 2.58 |
Execution | ||||
---|---|---|---|---|
1st | 0.02 | 0.02 | 0.02 | 2.72 |
2nd | 0.02 | 0.02 | 0.02 | 2.70 |
Execution | ||||
---|---|---|---|---|
1st | 0.05 | 0.04 | 0.05 | 1.69 |
2nd | 0.06 | 0.04 | 0.05 | 1.67 |
4.14 | 4.61 | 5.14 | 1.00 | |
4.16 | 4.61 | 5.10 | 0.94 | |
4.17 | 4.62 | 5.10 | 0.93 | |
4.04 | 4.67 | 5.53 | 1.49 | |
4.04 | 4.67 | 5.53 | 1.49 | |
4.18 | 4.65 | 5.17 | 0.99 |
2.27 | 2.46 | 2.66 | 0.35 | |
2.26 | 2.47 | 2.70 | 0.44 | |
2.25 | 2.48 | 2.72 | 0.47 | |
2.15 | 2.55 | 3.07 | 0.92 | |
2.25 | 2.53 | 2.84 | 0.59 | |
2.28 | 2.54 | 2.80 | 0.52 |
2.38 | 2.59 | 2.84 | 0.46 | |
2.40 | 2.58 | 2.78 | 0.38 | |
2.42 | 2.59 | 2.78 | 0.36 | |
2.35 | 2.62 | 2.92 | 0.57 | |
2.35 | 2.59 | 2.89 | 0.54 | |
2.38 | 2.59 | 2.85 | 0.47 |
1.19 | 1.44 | 1.75 | 0.56 | |
1.14 | 1.41 | 1.73 | 0.59 | |
1.09 | 1.38 | 1.74 | 0.65 | |
1.24 | 1.65 | 2.16 | 0.92 | |
1.18 | 1.56 | 2.05 | 0.87 | |
1.15 | 1.51 | 2.08 | 0.87 |
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Bot, K.; Santos, S.; Laouali, I.; Ruano, A.; Ruano, M.d.G. Design of Ensemble Forecasting Models for Home Energy Management Systems. Energies 2021, 14, 7664. https://doi.org/10.3390/en14227664
Bot K, Santos S, Laouali I, Ruano A, Ruano MdG. Design of Ensemble Forecasting Models for Home Energy Management Systems. Energies. 2021; 14(22):7664. https://doi.org/10.3390/en14227664
Chicago/Turabian StyleBot, Karol, Samira Santos, Inoussa Laouali, Antonio Ruano, and Maria da Graça Ruano. 2021. "Design of Ensemble Forecasting Models for Home Energy Management Systems" Energies 14, no. 22: 7664. https://doi.org/10.3390/en14227664