When identifying and comparing forecasting models, there may be a risk that poorly selected criteria could lead to wrong conclusions. Thus, it is important to know how sensitive the results are to the selection of criteria. This contribution aims to study the sensitivity of the identification and comparison results to the choice of criteria. It compares typically applied criteria for tuning and performance assessment of load forecasting methods with estimated costs caused by the forecasting errors. The focus is on short-term forecasting of the loads of energy systems. The estimated costs comprise electricity market costs and network costs. We estimate the electricity market costs by assuming that the forecasting errors cause balancing errors and consequently balancing costs to the market actors. The forecasting errors cause network costs by overloading network components thus increasing losses and reducing the component lifetime or alternatively increase operational margins to avoid those overloads. The lifetime loss of insulators, and thus also the components, is caused by heating according to the law of Arrhenius. We also study consumer costs. The results support the assumption that there is a need to develop and use additional and case-specific performance criteria for electricity load forecasting.

We describe a neural network model of a municipal wastewater treatment plant (WWTP) in which on-line total solids (TS) sewer data generated by a novel microwave sensor is used as a model input variable The predictive performance of the model is compared with and without sewer data and with modelling with a traditional linear multiple linear regression (MLR) model In addition, the benefits of using neural networks are discussed According to our results, the neural network based MLP (multilayer perceptron) model provides a better estimate than the corresponding MLR model of WWTP effluent TS load The inclusion of sewer TS data as an input variable improved the performance of the models The results suggest that increased on-line sensing of WWTPs should be stressed and that neural networks are useful as a modelling tool due to their capability of handling the nonlinear and dynamic data of sewer and WWTP systems

Accurately forecasting power consumption is crucial important for efficient energy management. Machine learning (ML) models are often employed for this purpose. However, tuning their hyperparameters is a complex and time-consuming task. The article presents a novel multi-objective (MO) hybrid evolutionary-based approach, GA-SHADE-MO, for tuning ML models aimed at solving the complex problem of forecasting power consumption. The proposed algorithm simultaneously optimizes both hyperparameters and feature sets across six different ML models, ensuring enhanced accuracy and efficiency. The study focuses on predicting household power consumption at hourly and daily levels. The hybrid MO evolutionary algorithm integrates elements of genetic algorithms and self-adapted differential evolution. By incorporating MO optimization, GA-SHADE-MO balances the trade-offs between model complexity (the number of used features) and prediction accuracy, ensuring robust performance across various forecasting scenarios. Experimental numerical results show the superiority of the proposed method compared to traditional tuning techniques, and random search, showcasing significant improvements in predictive accuracy and computational efficiency. The findings suggest that the proposed GA-SHADE-MO approach offers a powerful tool for optimizing ML models in the context of energy consumption forecasting, with potential applications in other domains requiring precise predictive modeling. The study contributes to the advancement of ML optimization techniques, providing a framework that can be adapted and extended for various predictive analytics tasks.

Accurate prediction of energy consumption in district heating systems plays an important role in supporting effective and clean energy production and distribution in dense urban areas. Predictive models are needed for flexible and cost-effective operation of energy production and usage, e.g., using peak shaving or load shifting to compensate for heat losses in the pipeline. This helps to avoid exceedance of power plant capacity. The purpose of this study is to automate the process of building machine learning (ML) models to solve a short-term power demand prediction problem. The dataset contains a district heating network’s measured hourly power consumption and ambient temperature for 415 days. In this paper, we propose a hybrid evolutionary-based algorithm, named GA-SHADE, for the simultaneous optimization of ML models and feature selection. The GA-SHADE algorithm is a hybrid algorithm consisting of a Genetic Algorithm (GA) and success-history-based parameter adaptation for differential evolution (SHADE). The results of the numerical experiments show that the proposed GA-SHADE algorithm allows the identification of simplified ML models with good prediction performance in terms of the optimized feature subset and model hyperparameters. The main contributions of the study are (1) using the proposed GA-SHADE, ML models with varying numbers of features and performance are obtained. (2) The proposed GA-SHADE algorithm self-adapts during operation and has only one control parameter. There is no fine-tuning required before execution. (3) Due to the evolutionary nature of the algorithm, it is not sensitive to the number of features and hyperparameters to be optimized in ML models. In conclusion, this study confirms that each optimized ML model uses a unique set and number of features. Out of the six ML models considered, SVR and NN are better candidates and have demonstrated the best performance across several metrics. All numerical experiments were compared against the measurements and proven by the standard statistical tests.

Nowadays, solar energy is becoming one of the most popular sources of renewable energy worldwide. Traditional fossil fuels cause pollution and climate change, while solar power offers a clean and sustainable alternative. However, effective planning requires accurate prediction of the amount of solar energy that can be produced. Prediction accuracy directly depends on two factors: the model’s hyperparameters and the feature set. In this study, we use boosting models, such as LightGBM, XGBoost, and CatBoost, to forecast solar power production. The prediction horizon is 60 min, which corresponds to short-term forecasting. Model tuning is performed using the NSGA-II multi-objective optimization algorithm. In this study, NSGA-II simultaneously tunes hyperparameters and a feature set of boosting models. We aim to enhance the performance of the NSGA-II algorithm in the early stages using the proposed method to generate the initial population. The initialization is based on an ensemble of filtering methods. The proposed approach promotes faster convergence in the early stages of the algorithm compared to the traditional initialization method. The results of numerical experiments are proven by the Wilcoxon test.