MONIThoR - Monitoring and Optimizazion Innovative HOtel Resources management

This dataset supports the MONIThoR project (Monitoring and Optimization Innovative HOtel Resources management), which focuses on the monitoring and optimization of resource management in hotel buildings. It combines operational data collected through an energy monitoring system installed at a hotel in Northern Italy with the input datasets and computational results used to investigate and evaluate alternative operating strategies for combined heat and power (CHP) systems. The dataset is intended to support research on hotel energy performance, energy management, and predictive optimization, providing data for the analysis of building energy demand, energy production, and system operation under varying conditions. Monitoring Data The Monitoring folder contains CSV files with time-series measurements collected from the hotel's energy systems and indoor environments. The overall monitoring period extends from September 2025 to September 2026, although the temporal coverage varies across subsystems. The measurements cover several aspects of the building's energy performance and operation, including: Cogeneration and energy flows: CHP thermal power, electrical energy generation, gas consumption, photovoltaic production, electricity imported from the grid, and electricity exported to the grid. Electrical systems: Main electrical panel measurements, including currents, voltages, active and reactive power, electrical energy consumption, and power-quality indicators. Separate files also report electrical energy consumption for different building loads. Thermal systems: Thermal network measurements, control commands and setpoints, hot-water storage temperatures, and supply and return temperatures across heating, cooling, and other thermal circuits. Heating, ventilation, and air conditioning (HVAC): Air-conditioning circuit operation, equipment temperatures, and fan speeds. Indoor environmental conditions: Carbon dioxide concentration (CO2), indoor air quality (IAQ), particulate matter (PM2.5), temperature, relative humidity, and volatile organic compounds (VOC), monitored across several indoor areas. Weather and outdoor conditions: Global horizontal solar irradiance and cloud cover. Swimming pool: Water quality indicators, including chlorine concentration and pH, together with water temperature and quantity measurements. These data can support analyses of building energy performance, energy demand patterns, interactions between electrical and thermal systems, and the relationship between outdoor conditions, indoor environments, and energy consumption. Data and Results supporting the paper "A Rolling-Horizon MILP Framework for Combined Heat and Power Operation Optimization" The ScientificPaper folder contains the input datasets and computational results used to assess alternative CHP operating strategies. The ScientificPaper/Data subfolder includes: Real energy demand and photovoltaic data: Measured and predicted electrical demand, thermal demand, and photovoltaic production. Synthetic demand scenarios: Two hourly scenarios containing electrical demand, thermal demand, and photovoltaic production profiles. The supplied time series cover 1 January to 30 December 2025. Energy prices: Electricity purchase and selling prices and natural gas purchase prices, including real price series and variable price data. CHP performance characteristics: Theoretical operating curves for CHP units with nominal electrical capacities of 20 kW, 50 kW, and 70 kW, including electrical output, thermal output, and gas consumption at different operating regimes. The ScientificPaper/Results subfolder contains CSV files reporting the results of the operating strategy comparisons. The results cover the three CHP configurations (20 kW, 50 kW, and 70 kW) and five strategy labels (FL - Full Load, ML - Mixed Load, PF - Predictive Following, PO - Predictive Optimization and ZL - Zero Load). For real demand, the results cover three evaluation periods: 21 October–16 November 2025, March 2026, and April 2026. For synthetic demand, the results cover two scenarios over the supplied 2025 time series. Depending on the strategy, the output files report operating costs, CHP operating regimes, electricity sales and purchases, self-consumption, and additional operational and control variables. The PO results also include detailed information about optimization runs, operating transitions, and dispatch decisions. These datasets enable comparative evaluation of CHP operation under different demand profiles, price conditions, and plant capacities, supporting reproducibility and further research on energy management and predictive optimization of distributed energy systems. Additional Research The dataset also includes supplementary optimization results obtained through additional investigations beyond those presented in the main paper. These analyses explore the impact of battery energy storage, ideal forecasting conditions, and historical energy price variations on the performance of the considered CHP operating strategies. The AdditionalOptimization folder contains the results of additional optimization experiments considering two scenarios. The first investigates the integration of a battery energy storage system into the energy management framework, allowing the potential benefits of storage in coordinating energy production, consumption, and grid exchanges to be assessed. The second considers an idealized forecasting scenario in which predicted values are identical to the corresponding actual values, representing perfect forecasts. This provides a reference scenario for investigating the potential impact of forecasting accuracy on optimization performance and for assessing the effects of forecasting errors on operational decisions and economic outcomes. The VariablePricing folder contains the results obtained by evaluating the different operating strategies using historical electricity and natural gas market prices from 2022 to 2025. This analysis examines the performance of the strategies under different historical energy market conditions, providing insights into the sensitivity of optimization results to variations in electricity and natural gas prices and the robustness of the optimization framework across different market scenarios. Together, these supplementary results extend the analyses presented in the main paper and provide additional material for investigating the effects of energy storage, forecasting uncertainty, and changing energy prices on the economic performance of CHP-based energy management systems. Data format and temporal resolution All files are provided in CSV format and use semicolons (;) as field delimiters. Decimal values are represented using a dot (.). Time-series files include a timestamp column named Time. The data are predominantly hourly, although coverage and completeness vary by subsystem and file.