Guest Editorial: Special Issue on Short-Term Load Forecasting 2019, Results and Future Perspectives
- Short term load forecasting and distributed energy resources
- Short term load forecasting and demand aggregation levels
- Statistical forecasting models (SARIMA, ARMAX, exponential smoothing, linear and non-linear regression, and so on)
- Artificial neural networks (ANNs)
- Fuzzy regression models
- Tree-based regression methods
- Stacked and ensemble methods
- Evolutionary algorithms
- Deep learning architectures
- Support vector regression (SVR)
- Robust load forecasting
- Hierarchical and probabilistic forecasting
- Hybrid and combined models
3. Future Perspectives
- Distributed renewable energy generation, especially in local photovoltaic (PV), has grown intensively in recent years and, because of the current energy crisis, is expected to expand further in the near future. A high level of PV penetration in low- and medium-voltage grids can cause uncertainty in the operation and management processes carried out by utilities, because most meters register the net load, i.e., the difference between the actual load and the PV power generation behind the meter. Since utilities do not have access to the PV data (capacity, type of technology, physical layout, power generation, etc.) of their customers, load forecasting is becoming much more complex. The stochastic nature of solar-based generation will be combined with a change in the consumption habits of customers who own PV systems. These customers will be more likely to shift their consumption to daylight hours. New STLF methods must be developed achieving the proper accuracy even when the value of this distributed generation behind the meter remains unknown. This problem of difficulty in forecasting the net load may increase as small-scale electricity storage systems become cheaper.
- In recent years, an important change in the means of land transport has begun. On the one hand, the development of new technologies for more powerful electric batteries has made possible the manufacture of light vehicles with technically competitive electric propulsion compared to their counterparts with combustion engines. It is foreseeable that in the near future, not only will light vehicles such as cars be electric, but also large goods and passenger vehicles. Battery-based EVs represent a source of uncertainty for the grid, since it is not known in advance when they will be charged, nor the amount of energy they need, nor the time in which they will carry out this operation . On the other hand, there is the increased electrification in the transportation sector, which has been driven by environmental concerns. Both in the case of EVs with batteries, which will have to be recharged for their use, and in the case of the public transportation (buses, tramways and railways), each will have a significant influence on the change in future load patterns (e.g., faster oscillations of demand due to timetables and more frequent peaks due to the use of fast recharge stations). It will be necessary to explore new STLF methods that include explanatory variables specifically related to EV drivers, or develop methods with forecasting horizons and a granularity more adapted to the needs of the grids that facilitate these means of transport.
- The accuracy of the load forecasting depends on the data quality and the size of the load, among other factors. Results and conclusions of many papers are based on data from a specific system operator, country, region, or cover a short period of time, therefore those conclusions cannot be extrapolated to other regions or scenarios. It is necessary to create open benchmark databases, which include different load sizes and type of customers, to assess the accuracy of the forecasting methods from a more objective perspective.
- While there is considerable consensus on the use of certain measures to quantify the prediction error (e.g., RMSE or MAPE), it is also important to look more deeply into the economic or operational impact of such forecasting errors . Is the MAPE the best measure of accuracy to quantify the actual cost to the system operator caused by the forecast error? New accuracy indicators may be needed. In addition, it should be analyzed how they vary in the parameter tunning process, that is, how sensitive the forecasting method is to the selection of hyperparameters.
- It is well known that, apart from historical data, there are many factors that have a great impact on load forecasting, such as weather factors, calendar variables, electricity prices, etc. However, there is much to do in this context thanks to the current variety of data available (weather sensors, satellite images or social media platforms) with high granularity. Therefore, the analysis of new factors that can affect the load, their impact in the prediction model or even the development of reference lists of factors for each type of customer/load (residential, commercial, industrial, etc.) are worth exploring in future research papers.
- Machine learning (ML) methods are rising in many fields such as health, finances, people behavior, etc. As a result, adapting successful forecasting methods from those fields, the proposal of new ANN architectures and the development of new approaches (beyond current hybrid models) will be crucial to achieving significant progress in STLF. In this context, official competitions as well as collaborations among universities, startups, and electricity companies, represent suitable platforms for reaching a qualitative improvement in STLF.
- New STLF methods described in the literature have mostly been developed by academics, with very little involvement of industry professionals. Moreover, the proposed methods show only simulated results. The forecasting results of the proposed methods in real application scenarios, with an overview of the benefits obtained by the agent (system operator, owner, load aggregator, etc.) who uses the forecasts, the problems encountered in its implementation and the description of the solutions adopted to solve the drawbacks, would enrich the scientific contribution of the new STLF methods.
- The active customer is a cornerstone of new electricity markets, and this involves the increase in demand response levels. A fundamental requirement for the implementation and the verification of DR portfolio is the load forecasting of its participants, because DR changes the customer behavior and consequently forecasts. The improvement of STLF must have several and important advantages to engage, remain and increase the customer participation in DR. For instance, customers (and their aggregators) should obtain more stable revenues with improved customer baseline loads (CBL) that allow the right evaluation of their flexibility during DR periods. Some of these baseline attempts are based on STLF methodologies. Another consequence of the improvement of STLF is that customers, and aggregators, will be more “demand-balanced” and, in this way, they will need a fewer procurement of balance from third parties (e.g., balance service providers). Both advantages involve an improved cost-effectiveness of DR. Consequently, STLF methods aimed at DR actions should explore new explanatory variables such as the electricity price forecasts for the next hours or DR event signals triggered by operators.
- We are currently witnessing a change in the improvement of forecasting methods, where the interest of forecasting users is shifting from point to probabilistic forecasts . STLF emerges as an ideal field for the development of this approach. Probability forecasting methods provide forecasts with their associated uncertainty, which allows for quantification of the risk associated with a decision based on those forecasts. Uncertainty information associated with the forecast may be presented in the form of probability functions, quantiles or prediction intervals, which represents much more complete information than the value provided by point forecasting methods. An interesting application of probabilistic STLF methods may be their integration in DR programs, enablingutilities or aggregators to select the proper program and helping to determine at what time activate it.
Conflicts of Interest
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Gabaldón, A.; Ruiz-Abellón, M.C.; Fernández-Jiménez, L.A. Guest Editorial: Special Issue on Short-Term Load Forecasting 2019, Results and Future Perspectives. Energies 2022, 15, 9545. https://doi.org/10.3390/en15249545
Gabaldón A, Ruiz-Abellón MC, Fernández-Jiménez LA. Guest Editorial: Special Issue on Short-Term Load Forecasting 2019, Results and Future Perspectives. Energies. 2022; 15(24):9545. https://doi.org/10.3390/en15249545Chicago/Turabian Style
Gabaldón, Antonio, María Carmen Ruiz-Abellón, and Luis Alfredo Fernández-Jiménez. 2022. "Guest Editorial: Special Issue on Short-Term Load Forecasting 2019, Results and Future Perspectives" Energies 15, no. 24: 9545. https://doi.org/10.3390/en15249545