The majority of the existing studies on timber price forecasting are based on ARIMA/SARIMA autoregressive moving average models, while vector autoregressive (VAR) and exponential smoothing (ETS) models have been employed less often. To date, timber prices in primary timber markets have not been forecasted with ANN methodology. This methodology was used only for forecasting lumber futures. Low-labor-intensive and relatively simple solutions that can be used in practice as a tool supporting decisions of timber market participants were sought. The present work sets out to compare RBF and MLP artificial neural networks with the Prophet procedure and with classical models (i.e., ARIMA, ETS, BATS, and TBATS) in terms of their suitability for forecasting timber prices in Poland. The study material consisted of quarterly time series of net nominal prices of roundwood (W0) for the years 2005–2021. MLP was found to be far superior to other models in terms of forecasting price changes and levels. ANN models exhibited a better fit to minimum and maximum values as compared to the classical models, which had a tendency to smooth price trends and produce forecasts biased toward average values. The Prophet procedure led to the lowest quality of projections. Ex-post error-based measures of prediction accuracy revealed a complex picture. The best forecasts for alder wood were obtained using the ETS model (with RMSE and MAE values of approx. 0.38 € m⁻³). ETS also performed well with respect to beech timber, although in this case BATS was just as good in terms of RMSE, while the difference between ETS and neural models amounted to as little as 0.64 € m⁻³. Birch timber prices were most accurately predicted with BATS and TBATS models (MAE 0.86 € m⁻³, RMSE 1.04 € m⁻³). The prices of the most popular roundwood types in Poland, i.e., Scots pine, Norway spruce, and oaks, were best forecasted using ANNs, and especially MLP models. Among the neural models for oak (MAE 4.74 € m⁻³, RMSE 8.09 € m⁻³), pine (MAE 2.21 € m⁻³, RMSE 2.83 € m⁻³), beech (MAE 2.31 € m⁻³, RMSE 2.70 € m⁻³), alder (MAE 1.88 € m⁻³, RMSE 2.40 € m⁻³), and spruce (MAE 2.44 € m⁻³, RMSE 2.58 € m⁻³), the MLP model was the best (the RBF model for birch). Of the seven models used to forecast the prices of six types of wood, the worst results were obtained for oak wood, while the best results were obtained for alder.

The utilization of primary and secondary woody biomass resources, despite controversies, is being promoted to reduce dependence on fossil fuels and due to the need to diversify energy sources and ensure energy security in European Union countries. Forest biomass is one of the renewable and sustainable energy sources that can be used for electricity, heat, and biofuel production. In the context of the ongoing energy crisis in Europe, an attempt was made to analyze the production and consumption of woody biomass for energy purposes (fuel wood, chips, and pellets). Specifically, an analysis of similarities between European countries in terms of biomass utilization was conducted. The analysis was complemented by a forecast of primary biomass production in selected European countries. The similarity analysis was conducted using the Ward method. Artificial neural networks (ANNs), including multi-layer feedforward perceptron (MLP) and radial basis function (RBF) models, were used to predict fuelwood extraction. The study showed that woody biomass remains an important source of bioenergy in Europe, and its significance as a strategic resource guaranteeing energy security is likely to increase. Fuel wood harvesting in Europe generally shows an upward trend, particularly in the Czech Republic, Germany, Estonia, Denmark, and the UK. A decreasing trend was observed in France, Spain, Greece, and Cyprus. The analysis revealed differences between countries in terms of woody biomass consumption. The ANN-based forecasts of fuelwood supply generally showed an increase in primary biomass harvesting.

Currently, woodchips and logging residues form the greatest share of biomass fuels used to generate heat in combined heat and power plants. They are supplied from various regions of the EU. The calorific values of the wood species used as biomass may vary significantly depending on the moisture and composition of the fuel, harvest seasonality, location, and other factors. This article presents the main resources of forest biomass and its characteristic features, as well as the calorific value of woodchips depending on the moisture content. Our research is based on the source data of forest resources from the State Forests National Forest Holding (PGLLP) in Poland. The research conducted by the main forestry enterprise in Poland covered a period of four years. The data on the harvesting of woodchips and logging residues converted into the calorific values of biomass were based on our research and a review of reference publications. Standard methods were used in the research, which included an analysis of the species and assortment structure of the forest biomass of energetic significance that was available for use. The research showed that the moisture content of the woodchips and lump wood was about 30%. The average annual energy value of the wood in the total area of forest resources was 0.07 GJ/ha, whereas the highest value was 0.14 GJ/ha. Between 2018 and 2021, the average energy resources of forest biomass in Poland increased from 351.8 TJ to 498.4 TJ.

(1) Wood is a widely available raw material on the market, which satisfies the industrial demand and which is used both as a source of biomass for the wood materials industry in a broad sense and for energy-supplying purposes. These areas prove the functional values and the possibilities of the directional use of low-quality wood products. One of the factors influencing the overall balance of the wood biomass is the form and quality of the wood material that cannot be further processed mechanically. This study was conducted to determine the influence of this material by presenting the dependence between the level of wood biomass resources and the conditions of wood acquisition and processing in Poland. (2) The basic directions of biomass acquisition were verified in correlation with the level of its acquisition from forest areas and with the form of by-products generated by sawmills. The research was based on the data from reference publications and analysis of the processing of raw wood in sawmills. The research was conducted on raw hardwood and softwood from coniferous and deciduous forests in Poland. (3) The research confirmed the influence of the processing method on the form and share of by-products. It also showed that the form of the wood biomass obtained was influenced by the region of Poland. (4) The research showed that the regionalisation and wood processing directions were correlated with the structure of the wood biomass acquired.

The aim of the study was to test the applicability of forecasting in the analysis of the variability of prices and supply of wood in Poland. It relies on the autoregressive integrated model (ARIMA) that takes into account the level of cyclic, seasonal, and irregular fluctuations and the long-term trend as tools for the assessment of the predictions of the prices of selected medium-sized wood assortments. Elements of the time series were determined taking into account the cyclical character of the quarterly distribution. The data included quarterly information about the supply (amount) and prices (value) of wood sold by state forests in the years 2018–2022. The analysis was conducted for the most popular assortments: logging slash (M2, M2ZE), firewood S4, and medium-sized wood S2AP. In the period studied (years 2018–2022), the average rate of price variation was widely scattered. The average rate of price variation for the M2ZE assortment amounted to 7%. The average rate for M2 assortment was 1%, while the medium-sized S2AP assortment displayed the greatest variation of 99%. This means that between 2018 and the present, the price increased by nearly 100%. No major fluctuations were observed for the S4 assortment and its average rate of variation amounted to 0%. The analysis found seasonal variation was observed only for S4 firewood, the price of which went up each year in October, November, and December. For this reason, the forecast was made with the seasonal autoregressive integrated moving average (SARIMA) version of the model. It is difficult to forecast the price of wood due to variations in the market and the impact of global factors related to fluctuations in supply.