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The ability to forecast electricity generation for a small wind turbine is important both on a larger scale where there are many such turbines (because it creates problems for networks managed by distribution system operators) and for prosumers to allow current energy consumption planning. It is also important for owners of small energy systems in order to optimize the use of various energy sources and facilitate energy storage. The research presented here addresses an original, rarely predicted 48 h forecasting horizon for small wind turbines. This topic has been rather underrepresented in research, especially in comparison with forecasts for large wind farms. Wind speed forecasts with a 48 h horizon are also rarely used as input data. We have analyzed the available data to identify potentially useful explanatory variables for forecasting models. Eight sets with increasing data amounts were created to analyze the influence of the types and amounts of data on forecast quality. Hybrid, ensemble and single methods are used for predictions, including machine learning (ML) solutions like long short-term memory (LSTM), multi-layer perceptron (MLP), support vector regression (SVR) and K-nearest neighbours regression (KNNR). Original hybrid methods, developed for research of specific implementations and ensemble methods based on hybrid methods’ decreased errors of energy generation forecasts for small wind turbines in comparison with single methods. The “artificial neural network (ANN) type MLP as an integrator of ensemble based on hybrid methods” ensemble forecasting method incorporates an original combination of predictors. Predictions by this method have the lowest mean absolute error (MAE). In addition, this paper presents an original ensemble forecasting method, called “averaging ensemble based on hybrid methods without extreme forecasts”. Predictions by this method have the lowest root mean square error (RMSE) error among all tested methods. LSTM, a deep neural network, is the best single method, MLP is the second best one, while SVR, KNNR and, especially, linear regression (LR) perform less well. We prove that lagged values of forecasted time series slightly increase the accuracy of predictions. The same applies to seasonal and daily variability markers. Our studies have also demonstrated that using the full set of available input data and the best proposed hybrid and ensemble methods yield the lowest error. The proposed hybrid and ensemble methods are also applicable to other short-time generation forecasting in renewable energy sources (RES), e.g., in photovoltaic (PV) systems or hydropower.

The development of the existing water supply system in towns and villages involves the use of frequency converters and specialized control algorithms that can maintain the pressure level established in the supply grid within certain limits. The implementation of the standard scheme includes the elimination of the water tower, which, on the one hand, reduces the cost of its maintenance. On the other hand, in emergency situations, the water tower can still supply water to the consumer, which increases the time of uninterrupted water supply during repairs. The article reveals a method of increasing the energy efficiency of transporting drinking water with the help of a water tower by eliminating the excessive pressure of the water rise when using frequency converters. Our research that involved more than 300 pumping stations, allowed to establish the average value of the excessive pressure of water rise, which reached 0.36 MPa. For the wells under study, optimization of the pump operation modes revealed the energy-saving potential of up to 52% due to excessive pressure reduction and up to 2.0% due to a decrease in starting power at the time of engine acceleration.