This work presents CLASH (Computational Load Assessment daSHboard), a software service that operationalizes the methodology proposed by [1] for assessing the computational and energy costs of building load forecasting. CLASH extends the original approach by providing a service with an API and interactive dashboard for configuring the featured applications parameters, running the complete prediction pipeline, and visualizing the trade-offs between accuracy, execution time, and energy consumption. The backend implements REST endpoints that wrap the forecasting scripts, including context creation, outlier handling, dataset splitting, and prediction execution, while enabling parameter control through JSON configuration or API calls. Users can define the context periods (hourly or 5-min periods), the context moments (All day moments; Only activity times; Only night periods; Only night periods), and the processing unit (CPU/GPU). Additionally, users can upload datasets, and trigger both complete and stepwise forecasting. The frontend dashboard allows intuitive parameter selection, execution sequencing, and results exploration, including forecast outputs and computational metrics. As such, CLASH enables direct comparison of forecasting scenarios, highlighting cases where increased model accuracy incurs high computational or monitoring energy costs, confirming and extending the findings of the original study. Initial tests show that end-users can dynamically evaluate forecasting strategies, balancing predictive performance with sustainability considerations. By embedding green computing principles into a ready-to-use, API-accessible service, CLASH bridges the gap between research methodology and practical deployment. It empowers researchers to make informed, sustainability-oriented forecasting decisions without manual script execution or code modification.