| Title | Accuracy Analysis of Selected Time Series and Machine Learning Methods for Smart Cities based on Estonian Electricity Consumption Forecast |
|---|---|
| ID_Doc | 40672 |
| Authors | Häring, T; Ahmadiahangar, R; Rosin, A; Korotko, T; Biechl, H |
| Title | Accuracy Analysis of Selected Time Series and Machine Learning Methods for Smart Cities based on Estonian Electricity Consumption Forecast |
| Year | 2020 |
| Published | |
| Abstract | Increasing shares of renewable energy sources in combination with rising popularity of demand response applications and flexibility programs forces higher awareness for production and consumption balancing. Accurate models for forecasting are not just necessary for PV- or wind power sources in smart cities, but also the prediction of loads respectively consumption, which can be based on time series analysis or machine learning methods. Three of those methods, namely a linear regression (LM), a long short-term memory network (LSTM) and a neural network model (NN), have been selected to see their performance on predicting the load of a large smart city on the example of the Estonian electricity consumption data. Hourly data of the year 2019 was used as training data to predict the first 20 days of 2020. For this kind of prediction, the LM showed the lowest root mean square error (RMSE) and had the lowest computational time. The neural network was slightly less accurate. The LSTM showed the worst performance in terms of accuracy and computational time. Thus, LSTM is not the preferred method for this kind of prediction and the recommendation for forecasting such loads would be a LM because the RMSE and computational effort needed are lower than for a NN. |
Similar Articles
| ID | Score | Article |
|---|---|---|
42467
|
Fatema, I; Kong, XY; Fang, GF Electricity demand and price forecasting model for sustainable smart grid using comprehensive long short term memory(2021)International Journal Of Sustainable Engineering, 14, 6 | |
| 43022
|
Hong, SH; Seo, B; Jeon, HS; Choi, JM; Lee, KH; Rim, D Comparison of electricity savings in community units through ESS and PV generation using ANN-based prediction model under Korean climatic conditions(2024) | |
| 64440
|
Shahid, ZK; Saguna, S; Åhlund, C Forecasting Electricity and District Heating Consumption: A Case Study in Schools in Sweden(2023) | |
| 39359
|
Kim, D; Kwon, D; Park, L; Kim, J; Cho, S Multiscale LSTM-Based Deep Learning for Very-Short-Term Photovoltaic Power Generation Forecasting in Smart City Energy Management(2021)Ieee Systems Journal, 15, 1 | |
| 39821
|
Qiu, YF; Li, XZ; Zheng, W; Hu, QH; Wei, ZM; Yue, YQ The prediction of the impact of climatic factors on short-term electric power load based on the big data of smart city(2017) | |
| 36672
|
Jindal, A; Aujla, GS; Kumar, N; Prodan, R; Obaidat, MS DRUMS: Demand Response Management in a Smart City Using Deep Learning and SVR(2018) | |
| 64616
|
Buturache, AN; Stancu, S Solar Energy Production Forecast Using Standard Recurrent Neural Networks, Long Short-Term Memory, and Gated Recurrent Unit(2021)Inzinerine Ekonomika-Engineering Economics, 32, 4 | |
| 40350
|
Carrera, B; Kim, K A Regression Framework for Energy Consumption in Smart Cities with Encoder-Decoder Recurrent Neural Networks(2023)Energies, 16, 22 | |
| 14900
|
Luo, XJ; Oyedele, LO Forecasting building energy consumption: Adaptive long-short term memory neural networks driven by genetic algorithm(2021) | |
| 64397
|
Krechowicz, A; Krechowicz, M; Poczeta, K Machine Learning Approaches to Predict Electricity Production from Renewable Energy Sources(2022)Energies, 15, 23 |